Generalized Relation Modeling for Transformer Tracking

• URL: http://arxiv.org/abs/2303.16580v3
• Date: Fri, 21 Apr 2023 14:26:50 GMT
• Title: Generalized Relation Modeling for Transformer Tracking
• Authors: Shenyuan Gao, Chunluan Zhou, Jun Zhang
• Abstract summary: One-stream trackers let the template interact with all parts inside the search region throughout all the encoder layers. This could potentially lead to target-background confusion when the extracted feature representations are not sufficiently discriminative. We propose a generalized relation modeling method based on adaptive token division. Our method is superior to the two-stream and one-stream pipelines and achieves state-of-the-art performance on six challenging benchmarks with a real-time running speed.
• Score: 13.837171342738355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which allows earlier interaction between the template and search region, has achieved a remarkable performance gain. However, existing one-stream trackers always let the template interact with all parts inside the search region throughout all the encoder layers. This could potentially lead to target-background confusion when the extracted feature representations are not sufficiently discriminative. To alleviate this issue, we propose a generalized relation modeling method based on adaptive token division. The proposed method is a generalized formulation of attention-based relation modeling for Transformer tracking, which inherits the merits of both previous two-stream and one-stream pipelines whilst enabling more flexible relation modeling by selecting appropriate search tokens to interact with template tokens. An attention masking strategy and the Gumbel-Softmax technique are introduced to facilitate the parallel computation and end-to-end learning of the token division module. Extensive experiments show that our method is superior to the two-stream and one-stream pipelines and achieves state-of-the-art performance on six challenging benchmarks with a real-time running speed.

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