Vector-Decomposed Disentanglement for Domain-Invariant Object Detection

• URL: http://arxiv.org/abs/2108.06685v1
• Date: Sun, 15 Aug 2021 07:58:59 GMT
• Title: Vector-Decomposed Disentanglement for Domain-Invariant Object Detection
• Authors: Aming Wu, Rui Liu, Yahong Han, Linchao Zhu, Yi Yang
• Abstract summary: We try to disentangle domain-invariant representations from domain-specific representations. In the experiment, we evaluate our method on the single- and compound-target case.
• Score: 75.64299762397268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To improve the generalization of detectors, for domain adaptive object detection (DAOD), recent advances mainly explore aligning feature-level distributions between the source and single-target domain, which may neglect the impact of domain-specific information existing in the aligned features. Towards DAOD, it is important to extract domain-invariant object representations. To this end, in this paper, we try to disentangle domain-invariant representations from domain-specific representations. And we propose a novel disentangled method based on vector decomposition. Firstly, an extractor is devised to separate domain-invariant representations from the input, which are used for extracting object proposals. Secondly, domain-specific representations are introduced as the differences between the input and domain-invariant representations. Through the difference operation, the gap between the domain-specific and domain-invariant representations is enlarged, which promotes domain-invariant representations to contain more domain-irrelevant information. In the experiment, we separately evaluate our method on the single- and compound-target case. For the single-target case, experimental results of four domain-shift scenes show our method obtains a significant performance gain over baseline methods. Moreover, for the compound-target case (i.e., the target is a compound of two different domains without domain labels), our method outperforms baseline methods by around 4%, which demonstrates the effectiveness of our method.

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