Summary of Transformer with Controlled Attention For Synchronous Motion Captioning, by Karim Radouane and Sylvie Ranwez and Julien Lagarde and Andon Tchechmedjiev
Transformer with Controlled Attention for Synchronous Motion Captioning
by Karim Radouane, Sylvie Ranwez, Julien Lagarde, Andon Tchechmedjiev
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 This paper addresses the task of synchronous motion captioning, which aims to generate a language description synchronized with human motion sequences. This challenge has applications in aligned sign language transcription, unsupervised action segmentation, and temporal grounding. The proposed method introduces mechanisms to control self- and cross-attention distributions of the Transformer, allowing interpretability and time-aligned text generation. The approach utilizes masking strategies and structuring losses to maximize attention on important frames, preventing undesired information mixing and providing a monotonic attention distribution. This technique is demonstrated to be superior through evaluation on two benchmark datasets, KIT-ML and HumanML3D. Animated visual illustrations are provided in the code repository. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand human movements and describe them in words. This is useful for things like translating sign language into written text or identifying actions in videos. The researchers developed a new way to control how the computer looks at different parts of the movement, so it only focuses on what’s important. They tested this method with two big datasets and showed that it works better than other approaches. You can see animated examples of their work on their GitHub page. |
Keywords
» Artificial intelligence » Attention » Cross attention » Grounding » Text generation » Transformer » Unsupervised
