Progressive Feature Alignment for Unsupervised Domain Adaptation
Open accessAbstract
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.
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Multimodal image alignment provides spatial mapping between images taken by sensors of different modalities and can be categorized into feature-based and patch-based methods.
“Specifically, multimodal image alignment provides a spatial mapping capability between images taken by sensors of different modalities, which may be categorized into feature-based [167, 168] and patch-based [165, 166] methods.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [167]
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- Authors
- Chen, C., Xie, W., Huang, W., Rong, Y., Ding, X., Huang, Y., Xu, T., & Huang, J.
- Affiliations
- Xiamen University (CN)
- Tencent (China) (CN)
- The University of Texas at Arlington (US)
- ORCID
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 627-636
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963240485
- Cited by
- 481
- References
- 43
- Citations by year
- 2026: 13 · 2025: 63 · 2024: 75 · 2023: 87 · 2022: 87 · 2021: 100 · 2020: 48 · 2019: 8
- FWCI
- 23.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Tencent
- National Natural Science Foundation of China (81671674, 61671309, U1605252, 61571005, 61571382, 81671766)
- Natural Science Foundation of Fujian Province (2017J01126)
- Fundamental Research Funds for the Central Universities (20720180059, 20720160075)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- unsupervised domain adaptation, pseudo-labeling, error accumulation, intra-class variation
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