Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

• URL: http://arxiv.org/abs/2107.12636v1
• Date: Tue, 27 Jul 2021 07:17:12 GMT
• Title: Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
• Authors: Wen Wang, Yang Cao, Jing Zhang, Fengxiang He, Zheng-Jun Zha, Yonggang Wen, Dacheng Tao
• Abstract summary: We propose a novel Sequence Feature Alignment (SFA) method that is specially designed for the adaptation of detection transformers. SFA consists of a domain query-based feature alignment (DQFA) module and a token-wise feature alignment (TDA) module. Experiments on three challenging benchmarks show that SFA outperforms state-of-the-art domain adaptive object detection methods.
• Score: 141.70707071815653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detection transformers have recently shown promising object detection results and attracted increasing attention. However, how to develop effective domain adaptation techniques to improve its cross-domain performance remains unexplored and unclear. In this paper, we delve into this topic and empirically find that direct feature distribution alignment on the CNN backbone only brings limited improvements, as it does not guarantee domain-invariant sequence features in the transformer for prediction. To address this issue, we propose a novel Sequence Feature Alignment (SFA) method that is specially designed for the adaptation of detection transformers. Technically, SFA consists of a domain query-based feature alignment (DQFA) module and a token-wise feature alignment (TDA) module. In DQFA, a novel domain query is used to aggregate and align global context from the token sequence of both domains. DQFA reduces the domain discrepancy in global feature representations and object relations when deploying in the transformer encoder and decoder, respectively. Meanwhile, TDA aligns token features in the sequence from both domains, which reduces the domain gaps in local and instance-level feature representations in the transformer encoder and decoder, respectively. Besides, a novel bipartite matching consistency loss is proposed to enhance the feature discriminability for robust object detection. Experiments on three challenging benchmarks show that SFA outperforms state-of-the-art domain adaptive object detection methods. Code has been made available at: https://github.com/encounter1997/SFA.

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