Unsupervised and self-adaptative techniques for cross-domain person re-identification

• URL: http://arxiv.org/abs/2103.11520v1
• Date: Sun, 21 Mar 2021 23:58:39 GMT
• Title: Unsupervised and self-adaptative techniques for cross-domain person re-identification
• Authors: Gabriel Bertocco and Fernanda Andal\'o and Anderson Rocha
• Abstract summary: 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.
• Score: 82.54691433502335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Re-Identification (ReID) across non-overlapping cameras is a challenging task and, for this reason, most works in the prior art rely on supervised feature learning from a labeled dataset to match the same person in different views. However, it demands the time-consuming task of labeling the acquired data, prohibiting its fast deployment, specially in forensic scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. However, most UDA-based algorithms rely upon a complex loss function with several hyper-parameters, which hinders the generalization to different scenarios. Moreover, as UDA depends on the translation between domains, it is important to select the most reliable data from the unseen domain, thus avoiding error propagation caused by noisy examples on the target data -- an often overlooked problem. In this sense, we propose a novel UDA-based ReID method that optimizes a simple loss function with only one hyper-parameter and that takes advantage of triplets of samples created by a new offline strategy based on the diversity of cameras within a cluster. This new strategy adapts the model and also regularizes it, avoiding overfitting on the target domain. We also introduce a new self-ensembling strategy, in which weights from different iterations are aggregated to create a final model combining knowledge from distinct moments of the adaptation. For evaluation, we consider three well-known deep learning architectures and combine them for final decision-making. The proposed method does not use person re-ranking nor any label on the target domain, and outperforms the state of the art, with a much simpler setup, on the Market to Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation scenarios.

Related papers

• Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data [25.386114973556406]
  Multi-target unsupervised domain adaptation (UDA) aims to learn a unified model to address the domain shift between multiple target domains. Most existing solutions require labeled data from the source domain and unlabeled data from multiple target domains concurrently during training. We introduce a new strategy called "multi-target UDA without external data" for semantic segmentation.
  arXiv  Detail & Related papers  (2024-05-10T14:29:51Z)
• Fast One-Stage Unsupervised Domain Adaptive Person Search [17.164485293539833]
  Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations. We propose a Fast One-stage Unsupervised person Search (FOUS) which integrates complementary domain adaptaion with label adaptaion. FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW.
  arXiv  Detail & Related papers  (2024-05-05T07:15:47Z)
• Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation [84.82153655786183]
  We propose a novel framework called Informative Data Mining (IDM) to enable efficient one-shot domain adaptation for semantic segmentation. IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7%/55.4% on the GTA5/SYNTHIA to Cityscapes adaptation tasks.
  arXiv  Detail & Related papers  (2023-09-25T15:56:01Z)
• Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification [123.75412386783904]
  State-of-the-art object Re-ID approaches adopt clustering algorithms to generate pseudo-labels for the unlabeled target domain. We propose an uncertainty-aware clustering framework (UCF) for UDA tasks. Our UCF method consistently achieves state-of-the-art performance in multiple UDA tasks for object Re-ID.
  arXiv  Detail & Related papers  (2021-08-22T09:57:14Z)
• Multi-Domain Adversarial Feature Generalization for Person Re-Identification [52.835955258959785]
  We propose a multi-dataset feature generalization network (MMFA-AAE) It is capable of learning a universal domain-invariant feature representation from multiple labeled datasets and generalizing it to unseen' camera systems. It also surpasses many state-of-the-art supervised methods and unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2020-11-25T08:03:15Z)
• Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID [55.21702895051287]
  Domain adaptive object re-ID aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain. We propose a novel self-paced contrastive learning framework with hybrid memory. Our method outperforms state-of-the-arts on multiple domain adaptation tasks of object re-ID.
  arXiv  Detail & Related papers  (2020-06-04T09:12:44Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

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.