Routing Attention
Routing attention mechanisms are emerging as a key technique to improve the efficiency and performance of various deep learning models, particularly in computer vision. Current research focuses on developing dynamic routing strategies that selectively attend to relevant information, often employing hierarchical attention networks or multi-head approaches to process data more efficiently, reducing computational costs while maintaining or improving accuracy. This work is significant because it addresses the limitations of computationally expensive, fully connected attention mechanisms, leading to more efficient and scalable models for applications such as image processing, object detection, and video analysis.
Papers
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