PS: This project is going to be remotely supervised due to Covid-19
Sample activity segmentation:
Students.ProjectForm | |
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Title: | Weakly-Supervised Action Segmentation |
Abstract: | Activity understanding in videos became a popular research topic in the computer vision community because of its application to video analysis. Thanks to large-scale labeled video datasets[1,2], classifying activities in trimmed videos made significant progress in recent years. In contrast, action segmentation, which requires finding boundaries and action labels in untrimmed videos, is still a challenging problem. The key issue is untrimmed videos are usually quite long and contain multiple sub-activities, therefore gathering a large scale video dataset for action segmentation is time-consuming and cumbersome. To address these issues, recent research on this direction focuses on training deep architectures with weak labels. This project will focus on designing a new method for action segmentation with weak labels. The proposed approach will be compared against new SOTA methods [3,4,5] on action segmentation datasets. |
Student: | |
Director: | Federico Tombari |
Supervisor: | Huseyin Coskun, Prof. Nassir Navab |
Type: | DA/MA/BA |
Area: | Machine Learning, Computer Vision |
Status: | open |
Start: | |
Finish: | |
Thesis (optional): | |
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