  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.
