MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection Article Swipe
Vibashan VS
,
Vikram Gupta
,
Poojan Oza
,
Vishwanath A. Sindagi
,
Vishal M. Patel
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/cvpr46437.2021.00449
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/cvpr46437.2021.00449
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA). The proposed method consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features. However, since the category information is not available for the target samples, we propose to generate memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator. The proposed method is evaluated on several benchmark datasets and is shown to outperform existing approaches.
Related Topics
Concepts
Discriminative model
Computer science
Artificial intelligence
Discriminator
Domain (mathematical analysis)
Benchmark (surveying)
Feature (linguistics)
Pattern recognition (psychology)
Domain adaptation
Process (computing)
Object (grammar)
Machine learning
Categorization
Object detection
Classifier (UML)
Mathematics
Mathematical analysis
Geography
Philosophy
Operating system
Linguistics
Geodesy
Telecommunications
Detector
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/cvpr46437.2021.00449
- OA Status
- green
- Cited By
- 49
- References
- 73
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3135497965
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3135497965Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/cvpr46437.2021.00449Digital Object Identifier
- Title
-
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-01Full publication date if available
- Authors
-
Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A. Sindagi, Vishal M. PatelList of authors in order
- Landing page
-
https://doi.org/10.1109/cvpr46437.2021.00449Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.04224Direct OA link when available
- Concepts
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Discriminative model, Computer science, Artificial intelligence, Discriminator, Domain (mathematical analysis), Benchmark (surveying), Feature (linguistics), Pattern recognition (psychology), Domain adaptation, Process (computing), Object (grammar), Machine learning, Categorization, Object detection, Classifier (UML), Mathematics, Mathematical analysis, Geography, Philosophy, Operating system, Linguistics, Geodesy, Telecommunications, DetectorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
49Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 17, 2023: 13, 2022: 11, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
73Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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