Summary of Decoupled Prompt-adapter Tuning For Continual Activity Recognition, by Di Fu et al.
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition
by Di Fu, Thanh Vinh Vo, Haozhe Ma, Tze-Yun Leong
First submitted to arxiv on: 20 Jul 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 proposed Decoupled Prompt-Adapter Tuning (DPAT) framework is a novel approach to advanced continual action recognition, which is crucial for various applications such as surveillance systems, patient monitoring in healthcare, sports performance analysis, and human-AI collaboration. DPAT integrates adapters for capturing spatial-temporal information and learnable prompts to mitigate catastrophic forgetting through a decoupled training strategy. This framework balances the generalization benefits of prompt tuning with the plasticity provided by adapters in pretrained vision models, effectively addressing the challenge of maintaining model performance amidst continuous data evolution. DPAT achieves state-of-the-art performance across several challenging action recognition benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper proposes a new way to recognize and analyze actions in videos. This is important for things like monitoring patients, analyzing sports performances, and working with artificial intelligence (AI). The proposed method, called Decoupled Prompt-Adapter Tuning (DPAT), helps models remember what they learned before and adapt to new information without losing their abilities. DPAT performs better than other methods in recognizing actions. |
Keywords
» Artificial intelligence » Generalization » Prompt
