Person Retrieval
25 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Person Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
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RSTPReid
ITCPR dataset
Latest papers
Image-based human re-identification: Which covariates are actually (the most) important?
KaiyangZhou/deep-person-reid
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Image and Vision Computing 2024
Human re-identification (re-ID) is nowadays among the most popular topics in computer vision, due to the increasing importance given to safety/security in modern societies.
PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification
huyquoctrinh/pgs
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Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras.
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Firstly, we construct a new \textbf{dataset} named UFine6926.
Video-based Visible-Infrared Person Re-Identification with Auxiliary Samples
Previous methods focus on learning from cross-modality person images in different cameras.
Word4Per: Zero-shot Composed Person Retrieval
Searching for specific person has great social benefits and security value, and it often involves a combination of visual and textual information.
CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
In particular, Jaccard distance calculates the distance based on the overlap of relevant neighbors.
Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval
lin-kayla/subjectivity-sketch-reid
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2) Multi-perspective and multi-style.
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition
ashishjv1/LW-ALM
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Pedestrian Attribute Recognition (PAR) deals with the problem of identifying features in a pedestrian image.
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
Shuyu-XJTU/APTM
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To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text.
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision.
