  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.
