AiATrack: Attention in Attention for Transformer Visual Tracking

Shenyuan Gao, Chunluan Zhou, Chao Ma, Xinggang Wang, Junsong Yuan

Abstract:

Transformer trackers have achieved impressive advancements
recently, where the attention mechanism plays an important role. However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement. To address this issue, we propose an attention in attention (AiA) module, which enhances appropriate
correlations and suppresses erroneous ones by seeking consensus among
all correlation vectors. Our AiA module can be readily applied to both
self-attention blocks and cross-attention blocks to facilitate feature aggregation and information propagation for visual tracking. Moreover, we
propose a streamlined Transformer tracking framework, dubbed AiATrack, by introducing efficient feature reuse and target-background embeddings to make full use of temporal references. Experiments show that
our tracker achieves state-of-the-art performance on six tracking benchmarks while running at a real-time speed. Code and models are publicly
available at https://github.com/Little-Podi/AiATrack.