Summary of Lf-vit: Reducing Spatial Redundancy in Vision Transformer For Efficient Image Recognition, by Youbing Hu et al.
LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition
by Youbing Hu, Yun Cheng, Anqi Lu, Zhiqiang Cao, Dawei Wei, Jie Liu, Zhijun Li
First submitted to arxiv on: 8 Jan 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 Vision Transformer (ViT) excels in accuracy when handling high-resolution images, but it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT), which strategically curtails computational demands without impinging on performance. LF-ViT operates by processing a reduced-resolution image in the Localization phase, and if a definitive prediction remains elusive, it triggers Neighborhood Global Class Attention (NGCA) to identify class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT’s prowess: it remarkably decreases Deit-S’s FLOPs by 63% and concurrently amplifies throughput twofold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Vision Transformer is great at recognizing images, but it uses a lot of computer power and memory. To solve this problem, we created the Localization and Focus Vision Transformer (LF-ViT). LF-ViT reduces the amount of computer power needed without losing its ability to recognize things correctly. It works by looking at a smaller version of the image first, and if that’s not enough, it uses a special attention mechanism to find important parts of the image. Then, it uses those important parts to make a more accurate prediction. LF-ViT is efficient and effective, reducing computer usage by 63% while still getting great results. |
Keywords
» Artificial intelligence » Attention » Optimization » Vision transformer » Vit
