Multi-attention Associate Prediction Network for Visual Tracking
Xinglong Sun Haijiang Sun Shan Jiang Jiacheng Wang Xilai Wei Zhonghe Hu
Classification-regression prediction networks have realized impressive
success in several modern deep trackers. However, there is an inherent
difference between classification and regression tasks, so they have diverse
even opposite demands for feature matching. Existed models always ignore the
key issue and only employ a unified matching block in two task branches,
decaying the decision quality. Besides, these models also struggle with
decision misalignment situation. In this paper, we propose a multi-attention
associate prediction network (MAPNet) to tackle the above problems. Concretely,
two novel matchers, i.e., category-aware matcher and spatial-aware matcher, are
first designed for feature comparison by integrating self, cross, channel or
spatial attentions organically. They are capable of fully capturing the
category-related semantics for classification and the local spatial contexts
for regression, respectively. Then, we present a dual alignment module to
enhance the correspondences between two branches, which is useful to find the
optimal tracking solution. Finally, we describe a Siamese tracker built upon
the proposed prediction network, which achieves the leading performance on five
tracking benchmarks, consisting of LaSOT, TrackingNet, GOT-10k, TNL2k and
UAV123, and surpasses other state-of-the-art approaches.