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Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of the 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 contextdoi:10.1109/cvpr42600.2020.00676 dblp:conf/cvpr/YuXHS20 fatcat:aehpbl6cibe3ferjcnx6nvl2xy