Summary of Exploring Explainability in Video Action Recognition, by Avinab Saha et al.
Exploring Explainability in Video Action Recognition
by Avinab Saha, Shashank Gupta, Sravan Kumar Ankireddy, Karl Chahine, Joydeep Ghosh
First submitted to arxiv on: 13 Apr 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 focuses on understanding how trained deep neural networks make decisions in image classification and video action recognition tasks. While there have been efforts to explain network decisions in image classification, there has been a lack of exploration in video action recognition. The authors revisit Grad-CAM, a popular feature attribution method for image classification, and extend it to video action recognition. They introduce Video-TCAV, which quantifies the importance of specific concepts in decision-making processes. To generate spatial and spatiotemporal concepts relevant to video action recognition, the authors propose a machine-assisted approach. The paper demonstrates the importance of temporally-varying concepts by showing the superiority of dynamic spatiotemporal concepts over trivial spatial concepts. Overall, the work advances research in explainability of deep neural networks used in video action recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how a computer sees and understands videos. Right now, we don’t know exactly how it makes decisions about what’s happening in those videos. The authors of this paper want to change that by developing new ways to explain how computers make decisions in video action recognition tasks. They take a popular method used for images and adapt it for videos. They also create new tools to help generate ideas or concepts relevant to understanding videos. The results show that the computer’s decisions are influenced by changing patterns over time, not just single moments in the video. This research helps us better understand how computers process video information. |
Keywords
» Artificial intelligence » Image classification » Spatiotemporal
