DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913353482371072 |
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| author | Feng, Yongchao Li, Shiwei Gao, Yingjie Huang, Ziyue Zhang, Yanan Liu, Qingjie Wang, Yunhong |
| author_facet | Feng, Yongchao Li, Shiwei Gao, Yingjie Huang, Ziyue Zhang, Yanan Liu, Qingjie Wang, Yunhong |
| contents | Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capabilities in the target domain. Furthermore, these methods face a more formidable challenge in achieving consistent classification and localization in the target domain compared to the source domain. To overcome these challenges, we propose a novel Distillation-based Source Debiasing (DSD) framework for DAOD, which can distill domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related localization information from source and target-style mixed data. Accordingly, we present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation to further refine classification scores in the testing stage, achieving a harmonization between classification and localization. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10437 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection Feng, Yongchao Li, Shiwei Gao, Yingjie Huang, Ziyue Zhang, Yanan Liu, Qingjie Wang, Yunhong Computer Vision and Pattern Recognition Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capabilities in the target domain. Furthermore, these methods face a more formidable challenge in achieving consistent classification and localization in the target domain compared to the source domain. To overcome these challenges, we propose a novel Distillation-based Source Debiasing (DSD) framework for DAOD, which can distill domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related localization information from source and target-style mixed data. Accordingly, we present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation to further refine classification scores in the testing stage, achieving a harmonization between classification and localization. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works. |
| title | DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.10437 |