Visual attention network

🤖 Plain-English Summary

While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. It provides a novel method and a simple yet strong baseline for the community.

🔑 Key Findings

• However, the 2D nature of images brings three challenges for applying self-attention in computer vision: (1) treating images as 1D sequences neglects their 2D structures; (2) the quadratic complexity is too expensive for high-resolution images; (3) it only captures spatial adaptability but ignores channel adaptability.
• In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.
• Additionally, we present a neural network based on LKA, namely Visual Attention Network (VAN).

🔗 Related Resources