Core Concepts
RoPE demonstrates impressive extrapolation performance in Vision Transformers, leading to improved backbone performance.
Abstract
Introduction to Rotary Position Embedding (RoPE) and its application in Vision Transformers.
Comparison of RoPE with traditional position embeddings like APE and RPB.
Detailed analysis of RoPE-Mixed implementation for 2D vision data.
Experimental results showcasing the performance improvement of RoPE in various tasks like ImageNet-1k, COCO detection, and ADE20k segmentation.
Comparison with other multi-resolution methods like ResFormer.
Conclusion highlighting the effectiveness of RoPE in enhancing ViT performance across different tasks.
Stats
RoPEはVision Transformerで印象的な外挿性能を示し、バックボーンのパフォーマンス向上につながる。