Context-Aware Mixup for Domain Adaptive Semantic Segmentation Article Swipe
Qianyu Zhou
,
Zhengyang Feng
,
Qiqi Gu
,
Jiangmiao Pang
,
Guangliang Cheng
,
Xuequan Lu
,
Jianping Shi
,
Lizhuang Ma
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2108.03557
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2108.03557
Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependency, which is generally shared across different domains, leading to less-desired performance. In this paper, we propose a novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability toward the target domain. Firstly, we present a contextual mask generation strategy by leveraging the accumulated spatial distributions and prior contextual relationships. The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels. Besides, provided the context knowledge, we introduce a significance-reweighted consistency loss to penalize the inconsistency between the mixed student prediction and the mixed teacher prediction, which alleviates the negative transfer of the adaptation, e.g., early performance degradation. Extensive experiments and analysis demonstrate the effectiveness of our method against the state-of-the-art approaches on widely-used UDA benchmarks.
Related Topics
Concepts
Computer science
Segmentation
Context (archaeology)
Exploit
Dependency (UML)
Artificial intelligence
Domain (mathematical analysis)
Feature (linguistics)
Adaptation (eye)
Adaptability
Machine learning
Consistency (knowledge bases)
Mathematics
Psychology
Computer security
Ecology
Paleontology
Biology
Linguistics
Neuroscience
Mathematical analysis
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2108.03557
- https://arxiv.org/pdf/2108.03557
- OA Status
- green
- Cited By
- 13
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3190092061
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3190092061Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2108.03557Digital Object Identifier
- Title
-
Context-Aware Mixup for Domain Adaptive Semantic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-08Full publication date if available
- Authors
-
Qianyu Zhou, Zhengyang Feng, Qiqi Gu, Jiangmiao Pang, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang MaList of authors in order
- Landing page
-
https://arxiv.org/abs/2108.03557Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2108.03557Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2108.03557Direct OA link when available
- Concepts
-
Computer science, Segmentation, Context (archaeology), Exploit, Dependency (UML), Artificial intelligence, Domain (mathematical analysis), Feature (linguistics), Adaptation (eye), Adaptability, Machine learning, Consistency (knowledge bases), Mathematics, Psychology, Computer security, Ecology, Paleontology, Biology, Linguistics, Neuroscience, Mathematical analysis, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 2, 2022: 3, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
68Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2963073217, https://openalex.org/W2895281799, https://openalex.org/W2487365028, https://openalex.org/W2412782625, https://openalex.org/W2592691248, https://openalex.org/W3102977943, https://openalex.org/W2932414082, https://openalex.org/W2562192638, https://openalex.org/W1903029394, https://openalex.org/W2963136578, https://openalex.org/W3108566666, https://openalex.org/W2963052201, https://openalex.org/W3108560336, https://openalex.org/W3017324723, https://openalex.org/W3202509896, https://openalex.org/W2981512393, https://openalex.org/W3113960880, https://openalex.org/W2982378089, https://openalex.org/W2112796928, https://openalex.org/W2962808524, https://openalex.org/W3035256099, https://openalex.org/W2335728318, https://openalex.org/W2970971581, https://openalex.org/W3170700905, https://openalex.org/W3034679848, https://openalex.org/W3120562181, https://openalex.org/W3095697905, https://openalex.org/W3173206925, https://openalex.org/W2962976523, https://openalex.org/W2997310315, https://openalex.org/W2981429991, https://openalex.org/W2963120918, https://openalex.org/W3035294798, https://openalex.org/W3118969708, https://openalex.org/W3035236545, https://openalex.org/W2972285644, https://openalex.org/W2108598243, https://openalex.org/W3116730076, https://openalex.org/W2948959975, https://openalex.org/W2987385519, https://openalex.org/W3109470472, https://openalex.org/W3107590933, https://openalex.org/W2194775991, https://openalex.org/W3107502112, https://openalex.org/W3127902700, https://openalex.org/W2963107255, https://openalex.org/W3101468328, https://openalex.org/W2340897893, https://openalex.org/W2963449430, https://openalex.org/W2986831462, https://openalex.org/W2965711922, https://openalex.org/W3175308890, https://openalex.org/W2891728491, https://openalex.org/W2431874326, https://openalex.org/W1861492603, https://openalex.org/W2969893028, https://openalex.org/W3110486195, https://openalex.org/W3189754078, https://openalex.org/W2985406498, https://openalex.org/W2031489346, https://openalex.org/W2560023338, https://openalex.org/W2944141891, https://openalex.org/W2739759330, https://openalex.org/W3034272105, https://openalex.org/W3120804725, https://openalex.org/W3034417116, https://openalex.org/W2998607115, https://openalex.org/W3120800376 |
| referenced_works_count | 68 |
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| abstract_inverted_index.is | 48, 121 |
| abstract_inverted_index.of | 9, 40, 79, 167, 181 |
| abstract_inverted_index.on | 133, 188 |
| abstract_inverted_index.to | 5, 14, 55, 148 |
| abstract_inverted_index.we | 61, 100, 142 |
| abstract_inverted_index.The | 117 |
| abstract_inverted_index.UDA | 190 |
| abstract_inverted_index.all | 39 |
| abstract_inverted_index.and | 34, 113, 126, 157, 176 |
| abstract_inverted_index.for | 69, 91 |
| abstract_inverted_index.our | 182 |
| abstract_inverted_index.the | 10, 26, 44, 93, 96, 109, 129, 139, 150, 153, 158, 164, 168, 179, 185 |
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| abstract_inverted_index.mask | 104, 120 |
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| abstract_inverted_index.which | 47, 74, 162 |
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| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Quality Education |
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| citation_normalized_percentile.is_in_top_10_percent | False |