MixFormer: End-to-End Tracking with Iterative Mixed Attention

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. 2022: Yutao Cui, Jiang Cheng, Limin Wang, Gangshan Wu Ranked #1 on Visual Object Tracking on GOT-10k https://arxiv.org/pdf/2203.11082v1.pdf