Papers Unsupervised Person Re-Identification
“Unsupervised Person Re-Identification” 태그가 달린 논문 108편 · 필터 해제
A review of Recent Techniques for Person Re-Identification
Person re-identification (ReId), a crucial task in surveillance, involves matching individuals across different camera views. The advent of Deep Learning, especially supervised techniques like Convolutional Neural Networ…
Unsupervised Person Re-IdentificationTCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive Learning for Unsupervised Person Re-identification
This paper proposes the ViT Token Constraint and Multi-scale Memory bank (TCMM) method to address the patch noises and feature inconsistency in unsupervised person re-identification works. Many excellent methods use ViT …
Contrastive LearningPerson Re-IdentificationPseudo LabelUnsupervised Person Re-IdentificationAnti-Forgetting Adaptation for Unsupervised Person Re-identification
Regular unsupervised domain adaptive person re-identification (ReID) focuses on adapting a model from a source domain to a fixed target domain. However, an adapted ReID model can hardly retain previously-acquired knowled…
Domain Adaptive Person Re-IdentificationPerson Re-IdentificationUnsupervised Person Re-IdentificationPose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification
Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras. Supervised approaches require identity information that may be difficult to obtain and are inherently bia…
ClusteringPerson Re-IdentificationUnsupervised Person Re-Identification3C: Confidence-Guided Clustering and Contrastive Learning for Unsupervised Person Re-Identification
Unsupervised person re-identification (Re-ID) aims to learn a feature network with cross-camera retrieval capability in unlabelled datasets. Although the pseudo-label based methods have achieved great progress in Re-ID, …
ClusteringContrastive LearningPerson Re-IdentificationPseudo Label+1CORE-ReID: Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-Identification
This study introduces a novel framework, “Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)”, to address an Unsupervised Domain Adaptation (UD…
Domain AdaptationPerson Re-IdentificationUnsupervised Domain AdaptationUnsupervised Person Re-IdentificationAdaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification
The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID. However, The method of updating memory based on all samples does not fully utilize the ha…
ClusteringContrastive LearningPerson Re-IdentificationUnsupervised Person Re-IdentificationCamera-aware Label Refinement for Unsupervised Person Re-identification
Unsupervised person re-identification aims to retrieve images of a specified person without identity labels. Many recent unsupervised Re-ID approaches adopt clustering-based methods to measure cross-camera feature simila…
ClusteringPerson Re-IdentificationUnsupervised Person Re-IdentificationSpatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
Recent unsupervised person re-identification (re-ID) methods achieve high performance by leveraging fine-grained local context. These methods are referred to as part-based methods. However, most part-based methods obtain…
ClusteringHuman ParsingMetric LearningPerson Re-Identification+1CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID, Jaccard distance is a widely used distance metric, especially in re-ranking and clu…
Person Re-IdentificationPerson RetrievalRe-RankingUnsupervised Person Re-Identification+2Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification (Re-ID) across various supervision sett…
Contrastive LearningPerson Re-IdentificationPrompt LearningUnsupervised Person Re-Identification+1Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identification
With rapid advancements in depth sensors and deep learning, skeleton-based person re-identification (re-ID) models have recently achieved remarkable progress with many advantages. Most existing solutions learn single-lev…
Contrastive LearningPerson Re-IdentificationUnsupervised Person Re-IdentificationPopulation-Based Evolutionary Gaming for Unsupervised Person Re-identification
Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has dif…
DiversityKnowledge DistillationPerson Re-IdentificationUnsupervised Person Re-IdentificationSiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change
In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change. Existing unsupervised person re-id methods are mainly designed for short-term scenari…
Clothes Changing Person Re-IdentificationContrastive LearningPerson Re-IdentificationUnsupervised Clothes Changing Person Re-Identification+2Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification
Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature d…
ClusteringPerson Re-IdentificationUnsupervised Person Re-IdentificationLearning Transferable Pedestrian Representation from Multimodal Information Supervision
Recent researches on unsupervised person re-identification~(reID) have demonstrated that pre-training on unlabeled person images achieves superior performance on downstream reID tasks than pre-training on ImageNet. Howev…
AttributeContrastive LearningPerson Re-IdentificationPerson Search+2Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification
Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the relationship between module parameters of Re-ID framework and feature distribu…
ClusteringComputational EfficiencyContrastive LearningPerson Re-Identification+2Learning Invariance from Generated Variance for Unsupervised Person Re-identification
This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two a…
Contrastive LearningData AugmentationGenerative Adversarial NetworkPerson Re-Identification+2Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification
Most recent unsupervised person re-identification methods maintain a cluster uni-proxy for contrastive learning. However, due to the intra-class variance and inter-class similarity, the cluster uni-proxy is prone to …
Contrastive LearningPerson Re-IdentificationUnsupervised Person Re-IdentificationNeighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labe…
ClusteringPerson Re-IdentificationPerson RetrievalPseudo Label+2