| dc.description.abstract | In single object tracking, the hierarchical Vision Transformer (ViT) architectures usually perform worse than plain ViT among current trackers. At the same time, the network architectures of state-of-the-art trackers are distinct, and thus there is no general purposed network architecture. This paper presents HyperXTrack, the first backbone network architecture that is applied to interaction in visual tracking. In addition, the proposed backbone interacts spatio-temporal context, where spatial context is the multi-scale information and temporal context provides historical information. HyperXTrack proceeds global and local spatial interaction, and computation complexity is linear with image resolution. After correlating with local texture features, the contour of the entire object is interacting. Interaction backbone networks adopt the proposed attention mechanism and the classic stacking rule where convolutions are applied before attention mechanism. Finally, this thesis proposes lightweight re-pretraining strategy. After modifying the existing network MaxViT, this thesis uses the pre-trained MaxViT weights, and re-pretrains only one epoch. Then the network can transfer to the downstream tasks. The experimental results show that HyperXTrack surpasses OSTrack′s 71% in AO with 71.8% on the GOT-10k dataset. HyperXTrack using a hierarchical architecture only needs 30M parameters, which can surpass OSTrack architecture with 93M parameters. | en_US |