Person Retrieval
23 papers with code • 1 benchmarks • 2 datasets
Latest papers
PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification
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.
Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval
2) Multi-perspective and multi-style.
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition
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
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.
Co-Attention Aligned Mutual Cross-Attention for Cloth-Changing Person Re-Identification
In this paper, we first design a novel Shape Semantics Embedding (SSE) module to encode body shape semantic information, which is one of the essential clues to distinguish pedestrians when their clothes change.
Body Part-Based Representation Learning for Occluded Person Re-Identification
Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning.