ARL 39 – Research Assistant, Computer Vision/Machine Learning Researcher

Project Name
Zero-Shot Learning for Semantic Scene Recognition

Project Description
The project is going to be on analyzing images or videos using deep learning based zero-shot and/or few-shot learning techniques for semantic scene recognition applications, such as detection, action/activity recognition, segmentation, captioning, etc. In this project, we will develop novel and effective zero-shot/few-shot learning approaches that are required to handle numerous real world scenarios where datasets are sparsely provided.

Job Description
You’ll be working: 1) on a problem related to ‘zero-shot/few-shot learning’ on images and videos for semantic scene recognition including detection, action/activity recognition, segmentation, captioning, etc. 2) independently to carry out a literature survey on state of the art approaches and devise a novel method 3) towards publishing a paper at the end of the internship.

Preferred Skills
– The ability to write code (Python) for computer vision/machine learning techniques
– Be familiar with deep learning frameworks (PyTorch, Caffe, etc)
– An advanced degree in computer science or relevant (MS or PhD)
– Have previous exprience of implementing deep learning algorithms to solve problems in computer vision/machine learning

Apply now.

Go back to the summer research program list.