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 context information via spatial attention, and selectively
emphasizes interdependent channel-wise features with channel attention. The
cross-attention is capable of aggregating rich contextual inter-dependencies
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 the strong baseline, SiamRPN++ [24], by 0.464->0.537 and
0.415->0.470 EAO on VOT 2016 and 2018. Our code is available at:
this https URL

本文提出了一种名为 SiamAttn 的可变形注意力机制的 Siamese 追踪器，该机制通过自注意和交叉注意来计算特征，能够有效地更新目标模板和聚合丰富的上下文信息以进行更准确的视觉目标跟踪，并在六个基准测试中实现了最新的最优结果。