Summary of Design and Analysis Of Efficient Attention in Transformers For Social Group Activity Recognition, by Masato Tamura
Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition
by Masato Tamura
First submitted to arxiv on: 15 Apr 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 paper proposes a novel approach to social group activity recognition, which involves leveraging attention modules in transformers to generate social group features. Unlike existing methods that rely on region features of individuals, this method aggregates features for each group member without duplication, allowing for more accurate recognition of social groups and their activities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to recognize different social groups, like sports teams or friends hanging out at a park. This paper helps us do just that by using special computer models called transformers. These models are good at finding patterns in data, but they need help figuring out what’s important and what’s not. That’s where attention modules come in – they help the models focus on the right things, like individual group members. The paper shows how this approach can recognize social groups and their activities really well, even when there are lots of people involved. |
Keywords
» Artificial intelligence » Activity recognition » Attention
