mlsanityData to 14 Oct 2026

Feature Representation Learning for Unsupervised Cross-domain Image Retrieval

arXiv 2207.09721 ↗First posted 20 Jul 2022cs.CVcited by 17

Not on the map. Why?

Abstract (arXiv)

Current supervised cross-domain image retrieval methods can achieve excellent performance. However, the cost of data collection and labeling imposes an intractable barrier to practical deployment in real applications. In this paper, we investigate the unsupervised cross-domain image retrieval task, where class labels and pairing annotations are no longer a prerequisite for training. This is an extremely challenging task because there is no supervision for both in-domain feature representation learning and cross-domain alignment. We address both challenges by introducing: 1) a new cluster-wise contrastive learning mechanism to help extract class semantic-aware features, and 2) a novel distance-of-distance loss to effectively measure and minimize the domain discrepancy without any external supervision. Experiments on the Office-Home and DomainNet datasets consistently show the superior image retrieval accuracies of our framework over state-of-the-art approaches. Our source code can be found at https://github.com/conghuihu/UCDIR.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

22 of 34 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2022Generative Adversarial Networkscited by 5,627
  6. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  7. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
  8. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,845
  9. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  10. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
  11. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  12. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,331
10 more
  1. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,188
  2. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
  3. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 640
  4. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 612
  5. 2016Unsupervised Domain Adaptation with Residual Transfer Networkscited by 341
  6. 2018DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptationcited by 214
  7. 2020Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identificationcited by 110
  8. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  9. 2020Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labelscited by 24
  10. 2019Deep clustering with concrete k-meanscited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 7; 2025: 3; 2026: 1.

17 papers.

Sort: