Summary of Rotary Position Embedding For Vision Transformer, by Byeongho Heo et al.
Rotary Position Embedding for Vision Transformer
by Byeongho Heo, Song Park, Dongyoon Han, Sangdoo Yun
First submitted to arxiv on: 20 Mar 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 Rotary Position Embedding (RoPE) has been shown to improve performance on language models, particularly for length extrapolation of Transformers. This study explores the impact of RoPE on computer vision domains, specifically Vision Transformer (ViT). By applying practical implementations of RoPE to 2D vision data, the analysis reveals that RoPE demonstrates impressive extrapolation performance, maintaining precision while increasing image resolution at inference. The results show improved performance for ImageNet-1k, COCO detection, and ADE-20k segmentation. This study provides thorough guidelines on applying RoPE into ViT, promising improved backbone performance with minimal extra computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how a technique called Rotary Position Embedding (RoPE) can help improve computer vision models like Vision Transformer (ViT). RoPE has already been shown to work well for language tasks. The researchers tested RoPE on ViT and found that it makes the model better at handling larger images without losing accuracy. This could be useful for tasks like image recognition, object detection, and segmentation. |
Keywords
» Artificial intelligence » Embedding » Inference » Object detection » Precision » Vision transformer » Vit
