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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 3: AAAI-22 Technical Tracks 3

DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation

February 1, 2023

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Authors

Qi Xu

Shanghai Jiao Tong University


Liang Yao

Tencent YouTu


Zhengkai Jiang

Tencent YouTu


Guannan Jiang

CATL


Wenqing Chu

Tencent YouTu


Wenhui Han

Fudan University


Wei Zhang

CATL


Chengjie Wang

Tencent YouTu


Ying Tai

Tencent YouTu


DOI:

10.1609/aaai.v36i3.20193


Abstract:

Model generalization to the unseen scenes is crucial to real-world applications, such as autonomous driving, which requires robust vision systems. To enhance the model generalization, domain generalization through learning the domain-invariant representation has been widely studied. However, most existing works learn the shared feature space within multi-source domains but ignore the characteristic of the feature itself (e.g., the feature sensitivity to the domain-specific style). Therefore, we propose the Domain-invariant Representation Learning (DIRL) for domain generalization which utilizes the feature sensitivity as the feature prior to guide the enhancement of the model generalization capability. The guidance reflects in two folds: 1) Feature re-calibration that introduces the Prior Guided Attention Module (PGAM) to emphasize the insensitive features and suppress the sensitive features. 2): Feature whiting that proposes the Guided Feature Whiting (GFW) to remove the feature correlations which are sensitive to the domain-specific style. We construct the domain-invariant representation which suppresses the effect of the domain-specific style on the quality and correlation of the features. As a result, our method is simple yet effective, and can enhance the robustness of various backbone networks with little computational cost. Extensive experiments over multiple domains generalizable segmentation tasks show the superiority of our approach to other methods.

Topics: AAAI

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HOW TO CITE:

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation AAAI 2022, 2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai (2022). DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai. DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai. 2022. DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai. (2022) "DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.2884-2892

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai, "DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation", AAAI, p.2884-2892, 2022.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai. "DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai. "DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 2884-2892.

Qi Xu||Liang Yao||Zhengkai Jiang||Guannan Jiang||Wenqing Chu||Wenhui Han||Wei Zhang||Chengjie Wang||Ying Tai. DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation. AAAI[Internet]. 2022[cited 2023]; 2884-2892.


ISSN: 2374-3468


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