DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection Article Swipe
Yongchao Feng
,
Shiwei Li
,
Yingjie Gao
,
Ziyue Huang
,
Yanan Zhang
,
Qingjie Liu
,
Yunhong Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.10437
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.10437
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.
Related Topics
Concepts
Computer science
Domain (mathematical analysis)
Consistency (knowledge bases)
Artificial intelligence
Object (grammar)
Process (computing)
Feature (linguistics)
Object detection
Data mining
Machine learning
Pattern recognition (psychology)
Domain adaptation
Mathematics
Classifier (UML)
Linguistics
Philosophy
Operating system
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.10437
- https://arxiv.org/pdf/2311.10437
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388843489
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388843489Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2311.10437Digital Object Identifier
- Title
-
DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-17Full publication date if available
- Authors
-
Yongchao Feng, Shiwei Li, Yingjie Gao, Ziyue Huang, Yanan Zhang, Qingjie Liu, Yunhong WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.10437Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.10437Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2311.10437Direct OA link when available
- Concepts
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Computer science, Domain (mathematical analysis), Consistency (knowledge bases), Artificial intelligence, Object (grammar), Process (computing), Feature (linguistics), Object detection, Data mining, Machine learning, Pattern recognition (psychology), Domain adaptation, Mathematics, Classifier (UML), Linguistics, Philosophy, Operating system, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.works. | 173 |
| abstract_inverted_index.Network | 99 |
| abstract_inverted_index.acquire | 24 |
| abstract_inverted_index.between | 145 |
| abstract_inverted_index.distill | 76 |
| abstract_inverted_index.domain. | 35, 58 |
| abstract_inverted_index.further | 134 |
| abstract_inverted_index.method, | 160 |
| abstract_inverted_index.methods | 8, 38 |
| abstract_inverted_index.present | 115 |
| abstract_inverted_index.propose | 64 |
| abstract_inverted_index.teacher | 82 |
| abstract_inverted_index.testing | 140 |
| abstract_inverted_index.(TROLN), | 100 |
| abstract_inverted_index.Adaptive | 4 |
| abstract_inverted_index.achieved | 10 |
| abstract_inverted_index.baseline | 166 |
| abstract_inverted_index.compared | 54 |
| abstract_inverted_index.detector | 21 |
| abstract_inverted_index.domains. | 90 |
| abstract_inverted_index.existing | 171 |
| abstract_inverted_index.impeding | 28 |
| abstract_inverted_index.improves | 163 |
| abstract_inverted_index.manifest | 155 |
| abstract_inverted_index.margins, | 169 |
| abstract_inverted_index.overcome | 60 |
| abstract_inverted_index.Debiasing | 69 |
| abstract_inverted_index.Detection | 6 |
| abstract_inverted_index.Enhancing | 119 |
| abstract_inverted_index.Extensive | 149 |
| abstract_inverted_index.achieving | 45, 142 |
| abstract_inverted_index.addition, | 92 |
| abstract_inverted_index.challenge | 43 |
| abstract_inverted_index.conducted | 153 |
| abstract_inverted_index.framework | 71 |
| abstract_inverted_index.improving | 84 |
| abstract_inverted_index.knowledge | 78 |
| abstract_inverted_index.progress, | 12 |
| abstract_inverted_index.strategy, | 121 |
| abstract_inverted_index.consistent | 46 |
| abstract_inverted_index.detector's | 86 |
| abstract_inverted_index.formidable | 42 |
| abstract_inverted_index.formulated | 127 |
| abstract_inverted_index.knowledge, | 27 |
| abstract_inverted_index.remarkable | 11 |
| abstract_inverted_index.Consistency | 118 |
| abstract_inverted_index.challenges, | 62 |
| abstract_inverted_index.experiments | 150 |
| abstract_inverted_index.information | 106, 125 |
| abstract_inverted_index.performance | 87 |
| abstract_inverted_index.pre-trained | 81 |
| abstract_inverted_index.Accordingly, | 113 |
| abstract_inverted_index.Domain-aware | 117 |
| abstract_inverted_index.Furthermore, | 36 |
| abstract_inverted_index.Localization | 98 |
| abstract_inverted_index.capabilities | 31 |
| abstract_inverted_index.consistently | 162 |
| abstract_inverted_index.localization | 49, 105, 131 |
| abstract_inverted_index.target-style | 110 |
| abstract_inverted_index.effectiveness | 157 |
| abstract_inverted_index.harmonization | 144 |
| abstract_inverted_index.localization. | 148 |
| abstract_inverted_index.outperforming | 170 |
| abstract_inverted_index.classification | 47, 136, 146 |
| abstract_inverted_index.generalization | 30 |
| abstract_inverted_index.representation | 132 |
| abstract_inverted_index.target-related | 104 |
| abstract_inverted_index.Target-Relevant | 96 |
| abstract_inverted_index.alignment-based | 172 |
| abstract_inverted_index.domain-agnostic | 77 |
| abstract_inverted_index.source-specific | 26 |
| abstract_inverted_index.feature-alignment | 1 |
| abstract_inverted_index.Distillation-based | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |