Deformable Siamese Attention Networks for Visual Object Tracking
Yuechen Yu, Yilei Xiong, Weilin Huang, Matthew R. Scott
Abstract
Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of target template and search image are computed independently in a Siamese architecture. In this paper, we propose Deformable Siamese Attention Networks, referred to as SiamAttn, by introducing a new Siamese attention mechanism that computes deformable self-attention and cross-attention. The self-attention learns strong context information via spatial attention, and selectively emphasizes interdependent channel-wise features with channel attention. The crossattention is capable of aggregating rich contextual interdependencies between the target template and the search image, providing an implicit manner to adaptively update the target template. In addition, we design a region refinement module that computes depth-wise cross correlations between the attentional features for more accurate tracking. We conduct experiments on six benchmarks, where our method achieves new state-of-the-art results, outperforming recent strong baseline, SiamRPN++, by 0.464 to 0.537 and 0.415 to 0.470 EAO on VOT 2016 and 2018.