TDAF: Top-Down Attention Framework for Vision Tasks

Bo Pang; Yizhuo Li; Jiefeng Li; Muchen Li; Hanwen Cao; Cewu Lu

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arXiv · 2012.07248

TDAF: Top-Down Attention Framework for Vision Tasks

Bo Pang·Yizhuo Li·Jiefeng Li·Muchen Li·Hanwen Cao·Cewu Lu

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Abstract

Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models. The designed Recursive Dual-Directional Nested Structure in it forms two sets of orthogonal paths, recursive and structural ones, where bottom-up spatial features and top-down attention features are extracted respectively. Such spatial and attention features are nested deeply, therefore, the proposed framework works in a mixed top-down and bottom-up manner. Empirical evidence shows that our TDAF can capture effective stratified attention information and boost performance. ResNet with TDAF achieves 2.0% improvements on ImageNet. For object detection, the performance is improved by 2.7% AP over FCOS. For pose estimation, TDAF improves the baseline by 1.6%. And for action recognition, the 3D-ResNet adopting TDAF achieves improvements of 1.7% accuracy.

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BibTeX RIS

Bo Pang, Yizhuo Li, Jiefeng Li, Muchen Li, Hanwen Cao, Cewu Lu. 2020-12-14. TDAF: Top-Down Attention Framework for Vision Tasks. https://arxiv.org/abs/2012.07248

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