Summary of Prototypical Calibrating Ambiguous Samples For Micro-action Recognition, by Kun Li et al.
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
by Kun Li, Dan Guo, Guoliang Chen, Chunxiao Fan, Jingyuan Xu, Zhiliang Wu, Hehe Fan, Meng Wang
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Micro-Action Recognition (MAR) has gained attention for its role in non-verbal communication and emotion analysis, with potential applications in human interaction. However, current approaches overlook the ambiguity in micro-actions due to category range and visual differences. This paper proposes a novel Prototypical Calibrating Ambiguous Network (PCAN) to mitigate MAR’s ambiguity. PCAN uses a hierarchical action-tree to categorize ambiguous samples into false negatives and false positives, then calibrates these samples by regulating distances between prototypes. The model also includes a prototypical diversity amplification loss to strengthen capacity and prototype-guided rectification for improved predictions. Experimental results demonstrate superior performance compared to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Micro-Action Recognition is like a secret language that helps us understand how people communicate without words. Right now, computers are not very good at recognizing these “micro-actions” because they don’t take into account the small differences between different actions. This paper presents a new way for computers to better recognize micro-actions by using a special network called PCAN. PCAN looks at each action and says whether it’s correct or not, then adjusts its answers based on how similar each action is to others like it. The results show that this new method works much better than the old ones. |
Keywords
» Artificial intelligence » Attention
