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

Tracking often uses a multistage pipeline of feature extraction, target information integration, and bounding box estimation. We also perform in-depth ablation studies to demonstrate the effectiveness of simultaneous feature extraction and information integration.

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• 1 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.
• 2 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.
• 3 This synchronous modeling scheme allows to extract target-specific discriminative features and perform extensive communication between target and search area.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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