Summary of Live and Learn: Continual Action Clustering with Incremental Views, by Xiaoqiang Yan et al.
Live and Learn: Continual Action Clustering with Incremental Views
by Xiaoqiang Yan, Yingtao Gan, Yiqiao Mao, Yangdong Ye, Hui Yu
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed novel continual action clustering (CAC) method leverages complementary information from different camera views to enhance clustering performance in a real-world scenario where camera views are incremental over time. It tackles challenges such as learning invariant information among multiple camera views, particularly in continual learning scenarios. The CAC consists of a category memory library that captures and stores learned categories from historical views, and a consensus partition matrix that can be updated using new camera views without requiring all previous views. This is achieved through a three-step alternate optimization process. Experimental results on 6 realistic multi-view action collections demonstrate the excellent clustering performance and time/space efficiency of CAC compared to 15 state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to group actions from different cameras together, using information from previous camera views. This helps when we only have access to one camera view at a time, which is common in real-world applications. The method uses two main components: a library that stores what’s been learned so far and an updated partition matrix that can adapt to new camera views without needing all the old ones. This makes it more efficient and effective than other methods. The results show that this approach outperforms many others on 6 real-world datasets. |
Keywords
» Artificial intelligence » Clustering » Continual learning » Optimization
