Summary of Token Cropr: Faster Vits For Quite a Few Tasks, by Benjamin Bergner et al.
Token Cropr: Faster ViTs for Quite a Few Tasks
by Benjamin Bergner, Christoph Lippert, Aravindh Mahendran
First submitted to arxiv on: 1 Dec 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 This paper proposes a novel token pruning method for Vision Transformers (ViTs) that achieves efficient inference in resource-constrained applications. The approach uses auxiliary prediction heads to learn task-relevant token selection, allowing for end-to-end training and removal after training. This method is evaluated on various vision tasks, including image classification, semantic segmentation, object detection, and instance segmentation, demonstrating speedups of 1.5-4x with minimal performance drops. As a best case, the method achieves a 2x speedup on the ADE20k semantic segmentation benchmark while maintaining negligible performance losses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computer vision models work faster and more efficiently without sacrificing their ability to do their job well. The scientists developed a new way to remove unimportant parts of these models, called tokens, which allows them to run much quicker on devices with limited resources. They tested this method on several different tasks, such as recognizing objects in images, and found that it can make the models up to 4 times faster without significantly affecting their performance. |
Keywords
» Artificial intelligence » Image classification » Inference » Instance segmentation » Object detection » Pruning » Semantic segmentation » Token
