Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks.

本文提出了一种新的transformer模型结构用于视觉目标跟踪，该模型引入了多尺度循环平移窗口机制使得跟踪注意力可以由像素级别提升到窗口级别，这种跨窗口的多尺度注意力有利于聚合不同尺度的注意力，同时采用的循环平移策略通过添加位置信息扩展窗口以提高跟踪准确度，并且减少了冗余计算，最后的实验表明这种方法取得了最先进的性能表现，并刷新了五个数据集的最佳记录。

具有循环移位窗口关注力的Transformer跟踪