Summary of Mustan: Multi-scale Temporal Context As Attention For Robust Video Foreground Segmentation, by Praveen Kumar Pokala et al.
MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation
by Praveen Kumar Pokala, Jaya Sai Kiran Patibandla, Naveen Kumar Pandey, Balakrishna Reddy Pailla
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the issue of overfitting in video foreground segmentation (VFS) tasks by leveraging both spatial and temporal cues from video data. Current methods rely solely on image-based approaches, which can lead to poor generalization performance. The authors propose a novel approach that integrates temporal context into VFS models using multi-scale attention mechanisms. They introduce two deep learning architectures, MUSTAN1 and MUSTAN2, which demonstrate improved performance on out-of-domain (OOD) data. To facilitate benchmarking and future research, the authors also present the Indoor Surveillance Dataset (ISD), featuring multiple annotations per frame. Experimental results show that the proposed methods significantly outperform baseline approaches on OOD data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a computer vision task called video foreground segmentation. It’s like trying to identify what’s moving in a video and what’s not. Current methods are good at this, but they can get stuck when they’re faced with new videos that look different from the ones they were trained on. The authors of this paper came up with a new way to do video foreground segmentation that takes into account both what’s happening now (spatial cues) and what happened before (temporal cues). They tested their approach using some new data they created, called the Indoor Surveillance Dataset, and it worked really well. This could help other researchers improve their own video analysis techniques. |
Keywords
» Artificial intelligence » Attention » Deep learning » Generalization » Overfitting
