Person Search
48 papers with code • 2 benchmarks • 9 datasets
Person Search is a task which aims at matching a specific person among a great number of whole scene images.
Source: Re-ID Driven Localization Refinement for Person Search
Datasets
CUHK-SYSU
PRW
MovieNet
ICFG-PEDES
Market1501-Attributes
DukeMTMC-attribute
CUHK-QA
Latest papers
Divide and Conquer: Hybrid Pre-training for Person Search
To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data.
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search
hcplab-sysu/personsearch-ctlg
•
•
Moreover, we propose a proximity data generation (PDG) module to automatically produce more diverse data for cross-modal training.
DDAM-PS: Diligent Domain Adaptive Mixer for Person Search
The objective of the two bridge losses is to guide the moderate mixed-domain representations to maintain an appropriate distance from both the source and target domain representations.
Ground-to-Aerial Person Search: Benchmark Dataset and Approach
yqc123456/hkd_for_person_search
•
•
In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31, 770 images of 260, 559 annotated bounding boxes for 2, 644 identities appearing in both of the UAVs and ground surveillance cameras.
An Empirical Study of CLIP for Text-based Person Search
TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS.
Learning Scene-Pedestrian Graph for End to end Person Search
Vill-Lab/2023-TII-SPG
•
•
IEEE Transactions on Industrial Informatics 2023
In this article, a novel scene-pedestrian graph (SPG) is proposed, which can explicitly model the interplay between the pedestrians and scenes.
Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos
crsm424/da-gss
•
•
In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images.
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Learning 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.
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
modelscope/modelscope
•
•
CVPR 2023
Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation.
