Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval

• URL: http://arxiv.org/abs/2505.13907v1
• Date: Tue, 20 May 2025 04:17:39 GMT
• Title: Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval
• Authors: Junyu Luo, Yusheng Zhao, Xiao Luo, Zhiping Xiao, Wei Ju, Li Shen, Dacheng Tao, Ming Zhang,
• Abstract summary: Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain.<n>Existing methods fail to address potential noise in the target domain, and directly align high-level features across domains.<n>We propose a novel Cross-Domain Diffusion with Progressive Alignment method (COUPLE) to address these challenges.
• Score: 52.67656818203429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, while maintaining low storage cost and high retrieval efficiency. However, existing methods typically fail to address potential noise in the target domain, and directly align high-level features across domains, thus resulting in suboptimal retrieval performance. To address these challenges, we propose a novel Cross-Domain Diffusion with Progressive Alignment method (COUPLE). This approach revisits unsupervised efficient domain adaptive retrieval from a graph diffusion perspective, simulating cross-domain adaptation dynamics to achieve a stable target domain adaptation process. First, we construct a cross-domain relationship graph and leverage noise-robust graph flow diffusion to simulate the transfer dynamics from the source domain to the target domain, identifying lower noise clusters. We then leverage the graph diffusion results for discriminative hash code learning, effectively learning from the target domain while reducing the negative impact of noise. Furthermore, we employ a hierarchical Mixup operation for progressive domain alignment, which is performed along the cross-domain random walk paths. Utilizing target domain discriminative hash learning and progressive domain alignment, COUPLE enables effective domain adaptive hash learning. Extensive experiments demonstrate COUPLE's effectiveness on competitive benchmarks.

Related papers

• Improving Domain Adaptation Through Class Aware Frequency Transformation [15.70058524548143]
  Most of the Unsupervised Domain Adaptation (UDA) algorithms focus on reducing the global domain shift between labelled source and unlabelled target domains. We propose a novel approach based on traditional image processing technique Class Aware Frequency Transformation (CAFT) CAFT utilizes pseudo label based class consistent low-frequency swapping for improving the overall performance of the existing UDA algorithms.
  arXiv  Detail & Related papers  (2024-07-28T18:16:41Z)
• Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation [9.875170018805768]
  Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. We propose a novel auxiliary task called Guidance Training. This task facilitates the effective utilization of cross-domain mixed sampling techniques while mitigating distribution shifts from the real world. We demonstrate the efficacy of our approach by integrating it with existing methods, consistently improving performance.
  arXiv  Detail & Related papers  (2024-03-22T07:12:48Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation [76.41664929948607]
  Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks. We present a novel and effective method to tackle this problem by using effective inter-domain and intra-domain semantic information propagation. Our source code and pre-trained models will be released soon.
  arXiv  Detail & Related papers  (2020-12-04T14:28:19Z)
• Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation [70.45936509510528]
  Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain. It is essential to align target data with only a small set of source data.
  arXiv  Detail & Related papers  (2020-08-26T03:18:53Z)
• Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment [11.74643883335152]
  Unsupervised domain adaptation is a promising avenue which transfers knowledge from a source domain to a target domain. We propose distance metric guided feature alignment (MetFA) to extract discriminative as well as domain-invariant features on both source and target domains. Our model integrates class distribution alignment to transfer semantic knowledge from a source domain to a target domain.
  arXiv  Detail & Related papers  (2020-08-19T13:36:57Z)
• Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
  Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
  arXiv  Detail & Related papers  (2020-03-04T23:25:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.