  Tracking often uses a multi-stage pipeline of feature extraction, target
information integration, and bounding box estimation. To simplify this pipeline
and unify the process of feature extraction and target information integration,
we present a compact tracking framework, termed as MixFormer, built upon
transformers. Our core design is to utilize the flexibility of attention
operations, and propose a Mixed Attention Module (MAM) for simultaneous feature
extraction and target information integration. This synchronous modeling scheme
allows to extract target-specific discriminative features and perform extensive
communication between target and search area. Based on MAM, we build our
MixFormer tracking framework simply by stacking multiple MAMs with progressive
patch embedding and placing a localization head on top. In addition, to handle
multiple target templates during online tracking, we devise an asymmetric
attention scheme in MAM to reduce computational cost, and propose an effective
score prediction module to select high-quality templates. Our MixFormer sets a
new state-of-the-art performance on five tracking benchmarks, including LaSOT,
TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L
achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on
VOT2020. We also perform in-depth ablation studies to demonstrate the
effectiveness of simultaneous feature extraction and information integration.
Code and trained models are publicly available at
https://github.com/MCG-NJU/MixFormer.
