Summary of Token Pruning Using a Lightweight Background Aware Vision Transformer, by Sudhakar Sah et al.
Token Pruning using a Lightweight Background Aware Vision Transformer
by Sudhakar Sah, Ravish Kumar, Honnesh Rohmetra, Ehsan Saboori
First submitted to arxiv on: 12 Oct 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 Background Aware Vision Transformer (BAViT) model addresses the limitations of high runtime memory and latency in vision transformer training and inference, particularly on edge devices. By leveraging segmentation maps and bounding box annotations to identify background tokens, BAViT can reduce memory usage and increase throughput through token pruning. The approach is specifically designed for Edge AI use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special kind of computer that helps cars see better. This computer needs lots of space and time to work well, which makes it hard to use on smaller devices like smartphones. Scientists found a way to make the computer smarter by getting rid of some unimportant information before using it. They called this new approach Background Aware Vision Transformer (BAViT). It can help computers process images faster while still keeping most of the important details. This is especially helpful for devices that need to be small and efficient, like those used in cars. |
Keywords
» Artificial intelligence » Bounding box » Inference » Pruning » Token » Vision transformer
