Summary of Skipvit: Speeding Up Vision Transformers with a Token-level Skip Connection, by Foozhan Ataiefard et al.
SkipViT: Speeding Up Vision Transformers with a Token-Level Skip Connection
by Foozhan Ataiefard, Walid Ahmed, Habib Hajimolahoseini, Saina Asani, Farnoosh Javadi, Mohammad Hassanpour, Omar Mohamed Awad, Austin Wen, Kangling Liu, Yang Liu
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed method, called SkipViT, aims to optimize vision transformers by reducing unnecessary computations. Vision transformers like ViT require processing all image tokens to learn relationships between them. However, many tokens contain irrelevant information, which is overlooked by the multi-head self-attention mechanism and the feed-forward network. The method separates these unimportant tokens and sends them through a low-cost computational path, without adding new parameters. Experimental results show that SkipViT can effectively drop 55% of tokens while achieving more than 13% training throughput and maintaining classification accuracy on the Huawei Ascend910A. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vision transformers are powerful tools for image recognition, but they can be slow and use a lot of data. The problem is that these models have to process every tiny part of an image, even if it’s just background or scenery. This makes them do extra work and take up more space. A new method called SkipViT helps solve this by finding the unimportant parts of the image and sending them through a special path that doesn’t use as much power. It works like magic! |
Keywords
* Artificial intelligence * Classification * Self attention * Vit
