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Domain Adaptation for Visual Applications: A Comprehensive Survey

arXiv 1702.05374 ↗First posted 17 Feb 2017cs.CVcited by 127

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Abstract (arXiv)

The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.

Key facts. Cited by 127 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2017.

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Citing papers per year: 2020: 1; 2021: 24; 2022: 32; 2023: 28; 2024: 25; 2025: 13; 2026: 4.

127 papers.

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  1. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  2. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  3. 2021MEMO: Test Time Robustness via Adaptation and Augmentationcited by 376
  4. 2022Test-Time Training with Masked Autoencoderscited by 208
  5. 2022Parameter-free Online Test-time Adaptationcited by 184
  6. 2021Hand-Object Contact Consistency Reasoning for Human Grasps Generationcited by 160
  7. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  8. 2021Model-Based Domain Generalizationcited by 103
  9. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  10. 2022Test-Time Adaptation via Self-Training with Nearest Neighbor Informationcited by 64
  11. 2021Transferable Semantic Augmentation for Domain Adaptationcited by 56
  12. 2021Deep Gait Recognition: A Surveycited by 54
38 more
  1. 2022CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentationcited by 54
  2. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  3. 2021DINE: Domain Adaptation from Single and Multiple Black-box Predictorscited by 51
  4. 2021One-Shot Generative Domain Adaptationcited by 41
  5. 2023StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalizationcited by 36
  6. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  7. 2023ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptationcited by 34
  8. 2024Foundation Models for Music: A Surveycited by 34
  9. 2021Geometric Unsupervised Domain Adaptation for Semantic Segmentationcited by 33
  10. 2021A Survey of Unsupervised Domain Adaptation for Visual Recognitioncited by 31
  11. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  12. 2021Confidence Calibration for Domain Generalization under Covariate Shiftcited by 27
  13. 2021In the light of feature distributions: moment matching for Neural Style Transfercited by 26
  14. 2021Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Datacited by 26
  15. 2021A Survey on Open Set Recognitioncited by 25
  16. 2022Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptationcited by 25
  17. 2023Federated Domain Generalization: A Surveycited by 25
  18. 2022The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Countingcited by 24
  19. 2021A Survey on Human-aware Robot Navigationcited by 23
  20. 2022Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensingcited by 21
  21. 2023Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentationcited by 20
  22. 2023When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlationscited by 18
  23. 2023Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairscited by 17
  24. 2022Improving Diversity with Adversarially Learned Transformations for Domain Generalizationcited by 16
  25. 2022Information-Theoretic Analysis of Unsupervised Domain Adaptationcited by 15
  26. 2023OpenForest: A data catalogue for machine learning in forest monitoringcited by 15
  27. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  28. 2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
  29. 2023Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protectioncited by 14
  30. 2021Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognitioncited by 13
  31. 2022Deep transfer learning for image classification: a surveycited by 13
  32. 2024Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)cited by 13
  33. 2022Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Surveycited by 12
  34. 2023Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection?cited by 10
  35. 2022Adaptive Domain Generalization via Online Disagreement Minimizationcited by 9
  36. 2023AGI for Agriculturecited by 9
  37. 2023DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Imagescited by 9
  38. 2024Test-time Adaptation for Regression by Subspace Alignmentcited by 9

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