Publications
Search
1.
Tran, Minh; Chang, Di; Siniukov, Maksim; Soleymani, Mohammad
Dyadic Interaction Modeling for Social Behavior Generation Miscellaneous
2024, (arXiv:2403.09069 [cs]).
@misc{tran_dyadic_2024,
title = {Dyadic Interaction Modeling for Social Behavior Generation},
author = {Minh Tran and Di Chang and Maksim Siniukov and Mohammad Soleymani},
url = {http://arxiv.org/abs/2403.09069},
year = {2024},
date = {2024-03-01},
urldate = {2024-03-19},
publisher = {arXiv},
abstract = {Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.},
note = {arXiv:2403.09069 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.
2.
Yu, Zifan; Tavakoli, Erfan Bank; Chen, Meida; You, Suya; Rao, Raghuveer; Agarwal, Sanjeev; Ren, Fengbo
TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection Miscellaneous
2024, (arXiv:2311.02535 [cs]).
@misc{yu_tokenmotion_2024,
title = {TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection},
author = {Zifan Yu and Erfan Bank Tavakoli and Meida Chen and Suya You and Raghuveer Rao and Sanjeev Agarwal and Fengbo Ren},
url = {http://arxiv.org/abs/2311.02535},
year = {2024},
date = {2024-02-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement. In this paper, we introduce TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by extracting motion-guided features using a learnable token selection. Evaluated on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art performance in VCOD. It outperforms the existing state-of-the-art method by a 12.8% improvement in weighted F-measure, an 8.4% enhancement in S-measure, and a 10.7% boost in mean IoU. The results demonstrate the benefits of utilizing motion-guided features via learnable token selection within a transformer-based framework to tackle the intricate task of VCOD.},
note = {arXiv:2311.02535 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement. In this paper, we introduce TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by extracting motion-guided features using a learnable token selection. Evaluated on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art performance in VCOD. It outperforms the existing state-of-the-art method by a 12.8% improvement in weighted F-measure, an 8.4% enhancement in S-measure, and a 10.7% boost in mean IoU. The results demonstrate the benefits of utilizing motion-guided features via learnable token selection within a transformer-based framework to tackle the intricate task of VCOD.
3.
Kwon, Deuksin; Weiss, Emily; Kulshrestha, Tara; Chawla, Kushal; Lucas, Gale M.; Gratch, Jonathan
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues Miscellaneous
2024, (arXiv:2402.13550 [cs]).
@misc{kwon_are_2024,
title = {Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues},
author = {Deuksin Kwon and Emily Weiss and Tara Kulshrestha and Kushal Chawla and Gale M. Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2402.13550},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
publisher = {arXiv},
abstract = {A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.},
note = {arXiv:2402.13550 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.
4.
Shi, Zhonghao; O'Connell, Allison; Li, Zongjian; Liu, Siqi; Ayissi, Jennifer; Hoffman, Guy; Soleymani, Mohammad; Matarić, Maja J.
Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education Miscellaneous
2024, (arXiv:2402.01647 [cs]).
@misc{shi_build_2024,
title = {Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education},
author = {Zhonghao Shi and Allison O'Connell and Zongjian Li and Siqi Liu and Jennifer Ayissi and Guy Hoffman and Mohammad Soleymani and Maja J. Matarić},
url = {http://arxiv.org/abs/2402.01647},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.},
note = {arXiv:2402.01647 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.
5.
Chang, Di; Shi, Yichun; Gao, Quankai; Fu, Jessica; Xu, Hongyi; Song, Guoxian; Yan, Qing; Yang, Xiao; Soleymani, Mohammad
MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer Miscellaneous
2023, (arXiv:2311.12052 [cs]).
@misc{chang_magicdance_2023,
title = {MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer},
author = {Di Chang and Yichun Shi and Quankai Gao and Jessica Fu and Hongyi Xu and Guoxian Song and Qing Yan and Xiao Yang and Mohammad Soleymani},
url = {http://arxiv.org/abs/2311.12052},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.},
note = {arXiv:2311.12052 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.
6.
Yang, Daniel; Kommineni, Aditya; Alshehri, Mohammad; Mohanty, Nilamadhab; Modi, Vedant; Gratch, Jonathan; Narayanan, Shrikanth
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models Miscellaneous
2023, (arXiv:2311.03551 [cs]).
@misc{yang_context_2023,
title = {Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models},
author = {Daniel Yang and Aditya Kommineni and Mohammad Alshehri and Nilamadhab Mohanty and Vedant Modi and Jonathan Gratch and Shrikanth Narayanan},
url = {http://arxiv.org/abs/2311.03551},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.},
note = {arXiv:2311.03551 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
7.
Cho, Hyundong; Liu, Shuai; Shi, Taiwei; Jain, Darpan; Rizk, Basem; Huang, Yuyang; Lu, Zixun; Wen, Nuan; Gratch, Jonathan; Ferrara, Emilio; May, Jonathan
Can Language Model Moderators Improve the Health of Online Discourse? Miscellaneous
2023, (arXiv:2311.10781 [cs]).
@misc{cho_can_2023,
title = {Can Language Model Moderators Improve the Health of Online Discourse?},
author = {Hyundong Cho and Shuai Liu and Taiwei Shi and Darpan Jain and Basem Rizk and Yuyang Huang and Zixun Lu and Nuan Wen and Jonathan Gratch and Emilio Ferrara and Jonathan May},
url = {http://arxiv.org/abs/2311.10781},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.},
note = {arXiv:2311.10781 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.
8.
Tran, Minh; Soleymani, Mohammad
Privacy-preserving Representation Learning for Speech Understanding Miscellaneous
2023, (arXiv:2310.17194 [eess]).
@misc{tran_privacy-preserving_2023,
title = {Privacy-preserving Representation Learning for Speech Understanding},
author = {Minh Tran and Mohammad Soleymani},
url = {http://arxiv.org/abs/2310.17194},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.},
note = {arXiv:2310.17194 [eess]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.
9.
Chawla, Kushal; Wu, Ian; Rong, Yu; Lucas, Gale M.; Gratch, Jonathan
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions Miscellaneous
2023, (arXiv:2310.14404 [cs]).
@misc{chawla_be_2023,
title = {Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions},
author = {Kushal Chawla and Ian Wu and Yu Rong and Gale M. Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2310.14404},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.},
note = {arXiv:2310.14404 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.
10.
Lukin, Stephanie M.; Pollard, Kimberly A.; Bonial, Claire; Hudson, Taylor; Arstein, Ron; Voss, Clare; Traum, David
Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release Miscellaneous
2023, (arXiv:2310.17568 [cs]).
@misc{lukin_navigating_2023,
title = {Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release},
author = {Stephanie M. Lukin and Kimberly A. Pollard and Claire Bonial and Taylor Hudson and Ron Arstein and Clare Voss and David Traum},
url = {http://arxiv.org/abs/2310.17568},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner. Participants issued spoken natural language instructions and received from the robot: text-based feedback, continuous 2D LIDAR mapping, and upon-request static photographs. We noticed that different strategies were adopted in terms of use of the modalities, and hypothesize that these differences may be correlated with success at several exploration sub-tasks. We found that requesting photos may have improved the identification and counting of some key entities (doorways in particular) and that this strategy did not hinder the amount of overall area exploration. Future work with larger samples may reveal the effects of more nuanced photo and dialogue strategies, which can inform the training of robotic agents. Additionally, we announce the release of our unique multi-modal corpus of human-robot communication in an exploration context: SCOUT, the Situated Corpus on Understanding Transactions.},
note = {arXiv:2310.17568 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner. Participants issued spoken natural language instructions and received from the robot: text-based feedback, continuous 2D LIDAR mapping, and upon-request static photographs. We noticed that different strategies were adopted in terms of use of the modalities, and hypothesize that these differences may be correlated with success at several exploration sub-tasks. We found that requesting photos may have improved the identification and counting of some key entities (doorways in particular) and that this strategy did not hinder the amount of overall area exploration. Future work with larger samples may reveal the effects of more nuanced photo and dialogue strategies, which can inform the training of robotic agents. Additionally, we announce the release of our unique multi-modal corpus of human-robot communication in an exploration context: SCOUT, the Situated Corpus on Understanding Transactions.
11.
Chang, Di; Yin, Yufeng; Li, Zongjian; Tran, Minh; Soleymani, Mohammad
LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis Miscellaneous
2023, (arXiv:2308.10713 [cs]).
@misc{chang_libreface_2023,
title = {LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis},
author = {Di Chang and Yufeng Yin and Zongjian Li and Minh Tran and Mohammad Soleymani},
url = {http://arxiv.org/abs/2308.10713},
year = {2023},
date = {2023-08-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be released at https://github.com/ihp-lab/LibreFace},
note = {arXiv:2308.10713 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be released at https://github.com/ihp-lab/LibreFace
12.
Yin, Yufeng; Chang, Di; Song, Guoxian; Sang, Shen; Zhi, Tiancheng; Liu, Jing; Luo, Linjie; Soleymani, Mohammad
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features Miscellaneous
2023, (arXiv:2308.12380 [cs]).
@misc{yin_fg-net_2023,
title = {FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features},
author = {Yufeng Yin and Di Chang and Guoxian Song and Shen Sang and Tiancheng Zhi and Jing Liu and Linjie Luo and Mohammad Soleymani},
url = {http://arxiv.org/abs/2308.12380},
year = {2023},
date = {2023-08-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {Automatic detection of facial Action Units (AUs) allows for objective facial expression analysis. Due to the high cost of AU labeling and the limited size of existing benchmarks, previous AU detection methods tend to overfit the dataset, resulting in a significant performance loss when evaluated across corpora. To address this problem, we propose FG-Net for generalizable facial action unit detection. Specifically, FG-Net extracts feature maps from a StyleGAN2 model pre-trained on a large and diverse face image dataset. Then, these features are used to detect AUs with a Pyramid CNN Interpreter, making the training efficient and capturing essential local features. The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model. Extensive experiments are conducted to evaluate within- and cross-corpus AU detection with the widely-used DISFA and BP4D datasets. Compared with the state-of-the-art, the proposed method achieves superior cross-domain performance while maintaining competitive within-domain performance. In addition, FG-Net is data-efficient and achieves competitive performance even when trained on 1000 samples. Our code will be released at textbackslashurlhttps://github.com/ihp-lab/FG-Net},
note = {arXiv:2308.12380 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Automatic detection of facial Action Units (AUs) allows for objective facial expression analysis. Due to the high cost of AU labeling and the limited size of existing benchmarks, previous AU detection methods tend to overfit the dataset, resulting in a significant performance loss when evaluated across corpora. To address this problem, we propose FG-Net for generalizable facial action unit detection. Specifically, FG-Net extracts feature maps from a StyleGAN2 model pre-trained on a large and diverse face image dataset. Then, these features are used to detect AUs with a Pyramid CNN Interpreter, making the training efficient and capturing essential local features. The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model. Extensive experiments are conducted to evaluate within- and cross-corpus AU detection with the widely-used DISFA and BP4D datasets. Compared with the state-of-the-art, the proposed method achieves superior cross-domain performance while maintaining competitive within-domain performance. In addition, FG-Net is data-efficient and achieves competitive performance even when trained on 1000 samples. Our code will be released at textbackslashurlhttps://github.com/ihp-lab/FG-Net
13.
Chen, Meida; Hu, Qingyong; Yu, Zifan; Thomas, Hugues; Feng, Andrew; Hou, Yu; McCullough, Kyle; Ren, Fengbo; Soibelman, Lucio
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset Miscellaneous
2022, (arXiv:2203.09065 [cs]).
@misc{chen_stpls3d_2022,
title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset},
author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues Thomas and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman},
url = {http://arxiv.org/abs/2203.09065},
year = {2022},
date = {2022-10-01},
urldate = {2023-08-22},
publisher = {arXiv},
abstract = {Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 $kmˆ2$ of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.},
note = {arXiv:2203.09065 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 $kmˆ2$ of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.
14.
Angelika-Nikita, Maria; Melo, Celso M.; Terada, Kazunori; Lucas, Gale; Gratch, Jonathan
The Impact of Partner Expressions on Felt Emotion in the Iterated Prisoner's Dilemma: An Event-level Analysis Miscellaneous
2022.
@misc{angelika-nikita_impact_2022,
title = {The Impact of Partner Expressions on Felt Emotion in the Iterated Prisoner's Dilemma: An Event-level Analysis},
author = {Maria Angelika-Nikita and Celso M. Melo and Kazunori Terada and Gale Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2207.00925},
doi = {10.48550/arXiv.2207.00925},
year = {2022},
date = {2022-07-01},
urldate = {2022-09-22},
publisher = {arXiv},
abstract = {Social games like the prisoner's dilemma are often used to develop models of the role of emotion in social decision-making. Here we examine an understudied aspect of emotion in such games: how an individual's feelings are shaped by their partner's expressions. Prior research has tended to focus on other aspects of emotion. Research on felt-emotion has focused on how an individual's feelings shape how they treat their partner, or whether these feelings are authentically expressed. Research on expressed-emotion has focused on how an individual's decisions are shaped by their partner's expressions, without regard for whether these expressions actually evoke feelings. Here, we use computer-generated characters to examine how an individual's moment-to-moment feelings are shaped by (1) how they are treated by their partner and (2) what their partner expresses during this treatment. Surprisingly, we find that partner expressions are far more important than actions in determining self-reported feelings. In other words, our partner can behave in a selfish and exploitive way, but if they show a collaborative pattern of expressions, we will feel greater pleasure collaborating with them. These results also emphasize the importance of context in determining how someone will feel in response to an expression (i.e., knowing a partner is happy is insufficient; we must know what they are happy-at). We discuss the implications of this work for cognitive-system design, emotion theory, and methodological practice in affective computing.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Social games like the prisoner's dilemma are often used to develop models of the role of emotion in social decision-making. Here we examine an understudied aspect of emotion in such games: how an individual's feelings are shaped by their partner's expressions. Prior research has tended to focus on other aspects of emotion. Research on felt-emotion has focused on how an individual's feelings shape how they treat their partner, or whether these feelings are authentically expressed. Research on expressed-emotion has focused on how an individual's decisions are shaped by their partner's expressions, without regard for whether these expressions actually evoke feelings. Here, we use computer-generated characters to examine how an individual's moment-to-moment feelings are shaped by (1) how they are treated by their partner and (2) what their partner expresses during this treatment. Surprisingly, we find that partner expressions are far more important than actions in determining self-reported feelings. In other words, our partner can behave in a selfish and exploitive way, but if they show a collaborative pattern of expressions, we will feel greater pleasure collaborating with them. These results also emphasize the importance of context in determining how someone will feel in response to an expression (i.e., knowing a partner is happy is insufficient; we must know what they are happy-at). We discuss the implications of this work for cognitive-system design, emotion theory, and methodological practice in affective computing.
15.
The Interservice Industry, Training, Simulation, and Education Conference Miscellaneous
0000.
@misc{noauthor_interservice_nodate,
title = {The Interservice Industry, Training, Simulation, and Education Conference},
url = {https://www.xcdsystem.com/iitsec/proceedings/index.cfm?Year=2021&AbID=97189&CID=862},
urldate = {2022-09-22},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
16.
APA PsycNet Miscellaneous
0000.
@misc{noauthor_apa_nodate,
title = {APA PsycNet},
url = {https://psycnet.apa.org/fulltext/2022-19957-001.html},
urldate = {2022-09-13},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Filter
2024
Tran, Minh; Chang, Di; Siniukov, Maksim; Soleymani, Mohammad
Dyadic Interaction Modeling for Social Behavior Generation Miscellaneous
2024, (arXiv:2403.09069 [cs]).
Abstract | Links | BibTeX | Tags: Virtual Humans
@misc{tran_dyadic_2024,
title = {Dyadic Interaction Modeling for Social Behavior Generation},
author = {Minh Tran and Di Chang and Maksim Siniukov and Mohammad Soleymani},
url = {http://arxiv.org/abs/2403.09069},
year = {2024},
date = {2024-03-01},
urldate = {2024-03-19},
publisher = {arXiv},
abstract = {Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.},
note = {arXiv:2403.09069 [cs]},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.
Yu, Zifan; Tavakoli, Erfan Bank; Chen, Meida; You, Suya; Rao, Raghuveer; Agarwal, Sanjeev; Ren, Fengbo
TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection Miscellaneous
2024, (arXiv:2311.02535 [cs]).
Abstract | Links | BibTeX | Tags: Computer Science - Computer Vision and Pattern Recognition, Narrative
@misc{yu_tokenmotion_2024,
title = {TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection},
author = {Zifan Yu and Erfan Bank Tavakoli and Meida Chen and Suya You and Raghuveer Rao and Sanjeev Agarwal and Fengbo Ren},
url = {http://arxiv.org/abs/2311.02535},
year = {2024},
date = {2024-02-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement. In this paper, we introduce TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by extracting motion-guided features using a learnable token selection. Evaluated on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art performance in VCOD. It outperforms the existing state-of-the-art method by a 12.8% improvement in weighted F-measure, an 8.4% enhancement in S-measure, and a 10.7% boost in mean IoU. The results demonstrate the benefits of utilizing motion-guided features via learnable token selection within a transformer-based framework to tackle the intricate task of VCOD.},
note = {arXiv:2311.02535 [cs]},
keywords = {Computer Science - Computer Vision and Pattern Recognition, Narrative},
pubstate = {published},
tppubtype = {misc}
}
The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement. In this paper, we introduce TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by extracting motion-guided features using a learnable token selection. Evaluated on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art performance in VCOD. It outperforms the existing state-of-the-art method by a 12.8% improvement in weighted F-measure, an 8.4% enhancement in S-measure, and a 10.7% boost in mean IoU. The results demonstrate the benefits of utilizing motion-guided features via learnable token selection within a transformer-based framework to tackle the intricate task of VCOD.
Kwon, Deuksin; Weiss, Emily; Kulshrestha, Tara; Chawla, Kushal; Lucas, Gale M.; Gratch, Jonathan
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues Miscellaneous
2024, (arXiv:2402.13550 [cs]).
Abstract | Links | BibTeX | Tags: AI, Virtual Humans
@misc{kwon_are_2024,
title = {Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues},
author = {Deuksin Kwon and Emily Weiss and Tara Kulshrestha and Kushal Chawla and Gale M. Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2402.13550},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
publisher = {arXiv},
abstract = {A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.},
note = {arXiv:2402.13550 [cs]},
keywords = {AI, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.
Shi, Zhonghao; O'Connell, Allison; Li, Zongjian; Liu, Siqi; Ayissi, Jennifer; Hoffman, Guy; Soleymani, Mohammad; Matarić, Maja J.
Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education Miscellaneous
2024, (arXiv:2402.01647 [cs]).
Abstract | Links | BibTeX | Tags: Virtual Humans
@misc{shi_build_2024,
title = {Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education},
author = {Zhonghao Shi and Allison O'Connell and Zongjian Li and Siqi Liu and Jennifer Ayissi and Guy Hoffman and Mohammad Soleymani and Maja J. Matarić},
url = {http://arxiv.org/abs/2402.01647},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.},
note = {arXiv:2402.01647 [cs]},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.
2023
Chang, Di; Shi, Yichun; Gao, Quankai; Fu, Jessica; Xu, Hongyi; Song, Guoxian; Yan, Qing; Yang, Xiao; Soleymani, Mohammad
MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer Miscellaneous
2023, (arXiv:2311.12052 [cs]).
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@misc{chang_magicdance_2023,
title = {MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer},
author = {Di Chang and Yichun Shi and Quankai Gao and Jessica Fu and Hongyi Xu and Guoxian Song and Qing Yan and Xiao Yang and Mohammad Soleymani},
url = {http://arxiv.org/abs/2311.12052},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.},
note = {arXiv:2311.12052 [cs]},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.
Yang, Daniel; Kommineni, Aditya; Alshehri, Mohammad; Mohanty, Nilamadhab; Modi, Vedant; Gratch, Jonathan; Narayanan, Shrikanth
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models Miscellaneous
2023, (arXiv:2311.03551 [cs]).
Abstract | Links | BibTeX | Tags: AI, UARC, Virtual Humans
@misc{yang_context_2023,
title = {Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models},
author = {Daniel Yang and Aditya Kommineni and Mohammad Alshehri and Nilamadhab Mohanty and Vedant Modi and Jonathan Gratch and Shrikanth Narayanan},
url = {http://arxiv.org/abs/2311.03551},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.},
note = {arXiv:2311.03551 [cs]},
keywords = {AI, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
Cho, Hyundong; Liu, Shuai; Shi, Taiwei; Jain, Darpan; Rizk, Basem; Huang, Yuyang; Lu, Zixun; Wen, Nuan; Gratch, Jonathan; Ferrara, Emilio; May, Jonathan
Can Language Model Moderators Improve the Health of Online Discourse? Miscellaneous
2023, (arXiv:2311.10781 [cs]).
Abstract | Links | BibTeX | Tags: AI, Dialogue, UARC, Virtual Humans
@misc{cho_can_2023,
title = {Can Language Model Moderators Improve the Health of Online Discourse?},
author = {Hyundong Cho and Shuai Liu and Taiwei Shi and Darpan Jain and Basem Rizk and Yuyang Huang and Zixun Lu and Nuan Wen and Jonathan Gratch and Emilio Ferrara and Jonathan May},
url = {http://arxiv.org/abs/2311.10781},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.},
note = {arXiv:2311.10781 [cs]},
keywords = {AI, Dialogue, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.
Tran, Minh; Soleymani, Mohammad
Privacy-preserving Representation Learning for Speech Understanding Miscellaneous
2023, (arXiv:2310.17194 [eess]).
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@misc{tran_privacy-preserving_2023,
title = {Privacy-preserving Representation Learning for Speech Understanding},
author = {Minh Tran and Mohammad Soleymani},
url = {http://arxiv.org/abs/2310.17194},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.},
note = {arXiv:2310.17194 [eess]},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.
Chawla, Kushal; Wu, Ian; Rong, Yu; Lucas, Gale M.; Gratch, Jonathan
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions Miscellaneous
2023, (arXiv:2310.14404 [cs]).
Abstract | Links | BibTeX | Tags: Dialogue, UARC, Virtual Humans
@misc{chawla_be_2023,
title = {Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions},
author = {Kushal Chawla and Ian Wu and Yu Rong and Gale M. Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2310.14404},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.},
note = {arXiv:2310.14404 [cs]},
keywords = {Dialogue, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.
Lukin, Stephanie M.; Pollard, Kimberly A.; Bonial, Claire; Hudson, Taylor; Arstein, Ron; Voss, Clare; Traum, David
Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release Miscellaneous
2023, (arXiv:2310.17568 [cs]).
Abstract | Links | BibTeX | Tags: Natural Language, UARC
@misc{lukin_navigating_2023,
title = {Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release},
author = {Stephanie M. Lukin and Kimberly A. Pollard and Claire Bonial and Taylor Hudson and Ron Arstein and Clare Voss and David Traum},
url = {http://arxiv.org/abs/2310.17568},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner. Participants issued spoken natural language instructions and received from the robot: text-based feedback, continuous 2D LIDAR mapping, and upon-request static photographs. We noticed that different strategies were adopted in terms of use of the modalities, and hypothesize that these differences may be correlated with success at several exploration sub-tasks. We found that requesting photos may have improved the identification and counting of some key entities (doorways in particular) and that this strategy did not hinder the amount of overall area exploration. Future work with larger samples may reveal the effects of more nuanced photo and dialogue strategies, which can inform the training of robotic agents. Additionally, we announce the release of our unique multi-modal corpus of human-robot communication in an exploration context: SCOUT, the Situated Corpus on Understanding Transactions.},
note = {arXiv:2310.17568 [cs]},
keywords = {Natural Language, UARC},
pubstate = {published},
tppubtype = {misc}
}
Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner. Participants issued spoken natural language instructions and received from the robot: text-based feedback, continuous 2D LIDAR mapping, and upon-request static photographs. We noticed that different strategies were adopted in terms of use of the modalities, and hypothesize that these differences may be correlated with success at several exploration sub-tasks. We found that requesting photos may have improved the identification and counting of some key entities (doorways in particular) and that this strategy did not hinder the amount of overall area exploration. Future work with larger samples may reveal the effects of more nuanced photo and dialogue strategies, which can inform the training of robotic agents. Additionally, we announce the release of our unique multi-modal corpus of human-robot communication in an exploration context: SCOUT, the Situated Corpus on Understanding Transactions.
Chang, Di; Yin, Yufeng; Li, Zongjian; Tran, Minh; Soleymani, Mohammad
LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis Miscellaneous
2023, (arXiv:2308.10713 [cs]).
Abstract | Links | BibTeX | Tags: Virtual Humans
@misc{chang_libreface_2023,
title = {LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis},
author = {Di Chang and Yufeng Yin and Zongjian Li and Minh Tran and Mohammad Soleymani},
url = {http://arxiv.org/abs/2308.10713},
year = {2023},
date = {2023-08-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be released at https://github.com/ihp-lab/LibreFace},
note = {arXiv:2308.10713 [cs]},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be released at https://github.com/ihp-lab/LibreFace
Yin, Yufeng; Chang, Di; Song, Guoxian; Sang, Shen; Zhi, Tiancheng; Liu, Jing; Luo, Linjie; Soleymani, Mohammad
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features Miscellaneous
2023, (arXiv:2308.12380 [cs]).
Abstract | Links | BibTeX | Tags: Virtual Humans
@misc{yin_fg-net_2023,
title = {FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features},
author = {Yufeng Yin and Di Chang and Guoxian Song and Shen Sang and Tiancheng Zhi and Jing Liu and Linjie Luo and Mohammad Soleymani},
url = {http://arxiv.org/abs/2308.12380},
year = {2023},
date = {2023-08-01},
urldate = {2024-02-21},
publisher = {arXiv},
abstract = {Automatic detection of facial Action Units (AUs) allows for objective facial expression analysis. Due to the high cost of AU labeling and the limited size of existing benchmarks, previous AU detection methods tend to overfit the dataset, resulting in a significant performance loss when evaluated across corpora. To address this problem, we propose FG-Net for generalizable facial action unit detection. Specifically, FG-Net extracts feature maps from a StyleGAN2 model pre-trained on a large and diverse face image dataset. Then, these features are used to detect AUs with a Pyramid CNN Interpreter, making the training efficient and capturing essential local features. The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model. Extensive experiments are conducted to evaluate within- and cross-corpus AU detection with the widely-used DISFA and BP4D datasets. Compared with the state-of-the-art, the proposed method achieves superior cross-domain performance while maintaining competitive within-domain performance. In addition, FG-Net is data-efficient and achieves competitive performance even when trained on 1000 samples. Our code will be released at textbackslashurlhttps://github.com/ihp-lab/FG-Net},
note = {arXiv:2308.12380 [cs]},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Automatic detection of facial Action Units (AUs) allows for objective facial expression analysis. Due to the high cost of AU labeling and the limited size of existing benchmarks, previous AU detection methods tend to overfit the dataset, resulting in a significant performance loss when evaluated across corpora. To address this problem, we propose FG-Net for generalizable facial action unit detection. Specifically, FG-Net extracts feature maps from a StyleGAN2 model pre-trained on a large and diverse face image dataset. Then, these features are used to detect AUs with a Pyramid CNN Interpreter, making the training efficient and capturing essential local features. The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model. Extensive experiments are conducted to evaluate within- and cross-corpus AU detection with the widely-used DISFA and BP4D datasets. Compared with the state-of-the-art, the proposed method achieves superior cross-domain performance while maintaining competitive within-domain performance. In addition, FG-Net is data-efficient and achieves competitive performance even when trained on 1000 samples. Our code will be released at textbackslashurlhttps://github.com/ihp-lab/FG-Net
2022
Chen, Meida; Hu, Qingyong; Yu, Zifan; Thomas, Hugues; Feng, Andrew; Hou, Yu; McCullough, Kyle; Ren, Fengbo; Soibelman, Lucio
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset Miscellaneous
2022, (arXiv:2203.09065 [cs]).
Abstract | Links | BibTeX | Tags: Narrative, UARC
@misc{chen_stpls3d_2022,
title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset},
author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues Thomas and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman},
url = {http://arxiv.org/abs/2203.09065},
year = {2022},
date = {2022-10-01},
urldate = {2023-08-22},
publisher = {arXiv},
abstract = {Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 $kmˆ2$ of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.},
note = {arXiv:2203.09065 [cs]},
keywords = {Narrative, UARC},
pubstate = {published},
tppubtype = {misc}
}
Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 $kmˆ2$ of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.
Angelika-Nikita, Maria; Melo, Celso M.; Terada, Kazunori; Lucas, Gale; Gratch, Jonathan
The Impact of Partner Expressions on Felt Emotion in the Iterated Prisoner's Dilemma: An Event-level Analysis Miscellaneous
2022.
Abstract | Links | BibTeX | Tags:
@misc{angelika-nikita_impact_2022,
title = {The Impact of Partner Expressions on Felt Emotion in the Iterated Prisoner's Dilemma: An Event-level Analysis},
author = {Maria Angelika-Nikita and Celso M. Melo and Kazunori Terada and Gale Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2207.00925},
doi = {10.48550/arXiv.2207.00925},
year = {2022},
date = {2022-07-01},
urldate = {2022-09-22},
publisher = {arXiv},
abstract = {Social games like the prisoner's dilemma are often used to develop models of the role of emotion in social decision-making. Here we examine an understudied aspect of emotion in such games: how an individual's feelings are shaped by their partner's expressions. Prior research has tended to focus on other aspects of emotion. Research on felt-emotion has focused on how an individual's feelings shape how they treat their partner, or whether these feelings are authentically expressed. Research on expressed-emotion has focused on how an individual's decisions are shaped by their partner's expressions, without regard for whether these expressions actually evoke feelings. Here, we use computer-generated characters to examine how an individual's moment-to-moment feelings are shaped by (1) how they are treated by their partner and (2) what their partner expresses during this treatment. Surprisingly, we find that partner expressions are far more important than actions in determining self-reported feelings. In other words, our partner can behave in a selfish and exploitive way, but if they show a collaborative pattern of expressions, we will feel greater pleasure collaborating with them. These results also emphasize the importance of context in determining how someone will feel in response to an expression (i.e., knowing a partner is happy is insufficient; we must know what they are happy-at). We discuss the implications of this work for cognitive-system design, emotion theory, and methodological practice in affective computing.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Social games like the prisoner's dilemma are often used to develop models of the role of emotion in social decision-making. Here we examine an understudied aspect of emotion in such games: how an individual's feelings are shaped by their partner's expressions. Prior research has tended to focus on other aspects of emotion. Research on felt-emotion has focused on how an individual's feelings shape how they treat their partner, or whether these feelings are authentically expressed. Research on expressed-emotion has focused on how an individual's decisions are shaped by their partner's expressions, without regard for whether these expressions actually evoke feelings. Here, we use computer-generated characters to examine how an individual's moment-to-moment feelings are shaped by (1) how they are treated by their partner and (2) what their partner expresses during this treatment. Surprisingly, we find that partner expressions are far more important than actions in determining self-reported feelings. In other words, our partner can behave in a selfish and exploitive way, but if they show a collaborative pattern of expressions, we will feel greater pleasure collaborating with them. These results also emphasize the importance of context in determining how someone will feel in response to an expression (i.e., knowing a partner is happy is insufficient; we must know what they are happy-at). We discuss the implications of this work for cognitive-system design, emotion theory, and methodological practice in affective computing.
0000
The Interservice Industry, Training, Simulation, and Education Conference Miscellaneous
0000.
@misc{noauthor_interservice_nodate,
title = {The Interservice Industry, Training, Simulation, and Education Conference},
url = {https://www.xcdsystem.com/iitsec/proceedings/index.cfm?Year=2021&AbID=97189&CID=862},
urldate = {2022-09-22},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
APA PsycNet Miscellaneous
0000.
@misc{noauthor_apa_nodate,
title = {APA PsycNet},
url = {https://psycnet.apa.org/fulltext/2022-19957-001.html},
urldate = {2022-09-13},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}