Summary of Lookupvit: Compressing Visual Information to a Limited Number Of Tokens, by Rajat Koner et al.
LookupViT: Compressing visual information to a limited number of tokens
by Rajat Koner, Gagan Jain, Prateek Jain, Volker Tresp, Sujoy Paul
First submitted to arxiv on: 17 Jul 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 paper introduces LookupViT, a novel vision transformer block that reduces the inference cost of Vision Transformers (ViT) while maintaining accuracy. The approach exploits information sparsity in images and videos by compressing high-resolution tokens to a fixed number of tokens, processing only these few compressed tokens while passing higher-resolution tokens through cheaper layers. This enables bidirectional cross-attention between token sets, offering advantages like easy implementation on standard ML accelerators, applicability to various tasks and tokenization approaches, and performance-computation trade-offs in a single trained model. LookupViT demonstrates effectiveness on image classification (ImageNet-1K and ImageNet-21K), video classification (Kinetics400 and Something-Something V2), and image captioning (COCO-Captions) with a frozen encoder, achieving 2reduction in FLOPs while maintaining or improving accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new kind of computer vision model called LookupViT. It’s designed to be faster and more efficient than other models that can do similar things. The idea is to look at the information in images and videos, and only use what you really need. This helps make the model run faster without losing any accuracy. The paper shows how well this new model works on different tasks like recognizing objects in pictures or understanding video captions. It’s also more robust than other models when dealing with noisy or distorted images. |
Keywords
» Artificial intelligence » Classification » Cross attention » Encoder » Image captioning » Image classification » Inference » Token » Tokenization » Vision transformer » Vit
