Summary of Multi-layer Learnable Attention Mask For Multimodal Tasks, by Wayner Barrios and Souyoung Jin
Multi-layer Learnable Attention Mask for Multimodal Tasks
by Wayner Barrios, SouYoung Jin
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 Learnable Attention Mask (LAM) addresses limitations in the Self-Attention mechanism by strategically regulating attention maps and prioritizing critical tokens in diverse settings, leveraging BERT-like transformer networks to capture associations between tokens. This extension enables multi-layer LAM, accommodating varied information aspects at each layer. Experimental validation on datasets like MADv2, QVHighlights, ImageNet 1K, and MSRVTT demonstrates the efficacy of LAM, enhancing model performance while reducing redundant computations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Learnable Attention Mask is a new approach that helps machines understand complex scenarios better. It’s an improvement to the Self-Attention mechanism in transformer models, which are good at understanding language. The new mask helps by focusing on important parts and ignoring less important ones, making it more efficient and accurate. This can be useful for tasks like movie understanding. |
Keywords
» Artificial intelligence » Attention » Bert » Mask » Self attention » Transformer
