Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification

• URL: http://arxiv.org/abs/2508.06831v1
• Date: Sat, 09 Aug 2025 05:10:22 GMT
• Title: Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
• Authors: Taha Mustapha Nehdi, Nairouz Mrabah, Atif Belal, Marco Pedersoli, Eric Granger,
• Abstract summary: This paper introduces a source-free Adaptive Gated Experts (SAGE-reID) method for person reID.<n>SAGE-reID is a cost-effective, source-free MSDA method that first trains individual source-specific low-rank adapters (LoRA) through source-free UDA.<n>Next, a lightweight gating network is introduced and trained to dynamically assign optimal merging weights for fusion of LoRA experts.
• Score: 14.267056872257564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) methods. Recent works show that when labeled data originates from several distinct sources (e.g., datasets and cameras), considering each source separately and applying multi-source domain adaptation (MSDA) typically yields higher accuracy and robustness compared to blending the sources and performing conventional UDA. However, state-of-the-art MSDA methods learn domain-specific backbone models or require access to source domain data during adaptation, resulting in significant growth in training parameters and computational cost. In this paper, a Source-free Adaptive Gated Experts (SAGE-reID) method is introduced for person reID. Our SAGE-reID is a cost-effective, source-free MSDA method that first trains individual source-specific low-rank adapters (LoRA) through source-free UDA. Next, a lightweight gating network is introduced and trained to dynamically assign optimal merging weights for fusion of LoRA experts, enabling effective cross-domain knowledge transfer. While the number of backbone parameters remains constant across source domains, LoRA experts scale linearly but remain negligible in size (<= 2% of the backbone), reducing both the memory consumption and risk of overfitting. Extensive experiments conducted on three challenging benchmarks: Market-1501, DukeMTMC-reID, and MSMT17 indicate that SAGE-reID outperforms state-of-the-art methods while being computationally efficient.

Related papers

• Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition [51.61979855488214]
  Personalized facial expression recognition (FER) involves adapting a machine learning model using samples from labeled sources and unlabeled target domains.<n>We propose a progressive MSDA approach that gradually introduces information from subjects based on their similarity to the target subject.<n>Our experiments show the effectiveness of our proposed method on pain datasets: Biovid and UNBC-McMaster.
  arXiv  Detail & Related papers  (2025-04-05T19:14:51Z)
• Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation [8.791916654073088]
  Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy. We propose Uncertainty-aware Adaptive Distillation (UAD) for the multi-source-free unsupervised domain adaptation setting.
  arXiv  Detail & Related papers  (2024-02-09T06:48:04Z)
• Subject-Based Domain Adaptation for Facial Expression Recognition [51.10374151948157]
  Adapting a deep learning model to a specific target individual is a challenging facial expression recognition task. This paper introduces a new MSDA method for subject-based domain adaptation in FER. It efficiently leverages information from multiple source subjects to adapt a deep FER model to a single target individual.
  arXiv  Detail & Related papers  (2023-12-09T18:40:37Z)
• Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation [13.260109561599904]
  Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to an unlabeled heterogeneous target domain. We propose "off-the-shelf (OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation.
  arXiv  Detail & Related papers  (2022-09-16T13:13:50Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Unsupervised Multi-Source Domain Adaptation for Person Re-Identification [39.817734080890695]
  Unsupervised domain adaptation (UDA) methods for person re-identification (re-ID) aim at transferring re-ID knowledge from labeled source data to unlabeled target data. We introduce the multi-source concept into UDA person re-ID field, where multiple source datasets are used during training. The proposed method outperforms state-of-the-art UDA person re-ID methods by a large margin, and even achieves comparable performance to the supervised approaches without any post-processing techniques.
  arXiv  Detail & Related papers  (2021-04-27T03:33:35Z)
• Unsupervised Multi-source Domain Adaptation Without Access to Source Data [58.551861130011886]
  Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. We propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model.
  arXiv  Detail & Related papers  (2021-04-05T10:45:12Z)
• Unsupervised and self-adaptative techniques for cross-domain person re-identification [82.54691433502335]
  Person Re-Identification (ReID) across non-overlapping cameras is a challenging task. Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
  arXiv  Detail & Related papers  (2021-03-21T23:58:39Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.