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Person Re-Identification
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Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TeLL Me what you cant see
CFReID: Continual Few-shot Person Re-Identification
Multi-modal Multi-platform Person Re-Identification: Benchmark and Method
Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identification
Clothes-Changing Person Re-identification Based On Skeleton Dynamics
Multi-Domain Biometric Recognition using Body Embeddings
AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification
SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks
A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification
From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization
TSDW: A Tri-Stream Dynamic Weight Network for Cloth-Changing Person Re-Identification
Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models
Exploring the Camera Bias of Person Re-identification
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification
CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
Human Re-ID Meets LVLMs: What can we expect?
Not Every Patch is Needed: Towards a More Efficient and Effective Backbone for Video-based Person Re-identification
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
From Cross-Modal to Mixed-Modal Visible-Infrared Re-Identification
Modality Unified Attack for Omni-Modality Person Re-Identification
TCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive Learning for Unsupervised Person Re-identification
Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration
Identity-aware Feature Decoupling Learning for Clothing-change Person Re-identification
Spectral Enhancement and Pseudo-Anchor Guidance for Infrared-Visible Person Re-Identification
Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification
IUST_PersonReId: A New Domain in Person Re-Identification Datasets
CLIP-Based Modality Compensation for Visible-Infrared Image Re-Identification
Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning
Relieving Universal Label Noise for Unsupervised Visible-Infrared Person Re-Identification by Inferring from Neighbors
Motif Guided Graph Transformer with Combinatorial Skeleton Prototype Learning for Skeleton-Based Person Re-Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.