Summary of Mixture Of Nested Experts: Adaptive Processing Of Visual Tokens, by Gagan Jain et al.
Mixture of Nested Experts: Adaptive Processing of Visual Tokens
by Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, Sujoy Paul
First submitted to arxiv on: 29 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 presents Mixture of Nested Experts (MoNE), a novel approach that leverages the redundancy in visual media such as images and videos to improve processing efficiency. MoNE builds upon the success of Vision Transformer (ViT) based models by introducing a nested structure for experts, allowing it to dynamically select tokens based on a priority order and utilize cheaper nested experts for redundant tokens. This framework enables MoNE to achieve equivalent performance to baseline models while reducing inference time compute by over two-fold. The approach is validated on standard image and video datasets such as ImageNet-21K, Kinetics400, and Something-Something-v2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make computer vision more efficient. It uses special kinds of neural networks called Mixture of Nested Experts (MoNE) that can process images and videos quickly while still getting good results. MoNE is different from other approaches because it learns to prioritize certain parts of the image or video over others, depending on how important they are. This allows it to use less powerful “experts” for less important parts, which makes it faster and more efficient. The paper shows that MoNE works well on a variety of images and videos, and it can even adapt to different situations. |
Keywords
» Artificial intelligence » Inference » Vision transformer » Vit
