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computer-vision-4

Person Search

3260 papers • 126 benchmarks • 313 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

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in person-search-4

Trend

Dataset

Best Model

Actions

CUHK-SYSU

CUHK-SYSU

PRW

PRW

Libraries

i

Use these libraries to find person-search-4 models and implementations

Datasets

CUHK-SYSU

PRW

MovieNet

ICFG-PEDES

P-DESTRE

Market1501-Attributes

Market1501-Attributes

Subtasks

No subtasks available.

Most implemented papers

Efficient Person Search: An Anchor-Free Approach

Shengcai Liao, Peng Zheng, Yichao Yan, Xiaokang Yang, Jinpeng Li, Jie Qin•Tue Aug 31 2021

This work presents an anchor-free approach to efficiently tackling person search, and names its framework as Feature-Aligned Person Search Network (AlignPS), which achieves state-of-the-art or competitive performance on two challenging person search benchmarks, but can be also extended to other challenging searching tasks such as animal and object search.

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Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

DukeMTMC-attribute

DukeMTMC-attribute

CUHK-QA

XKCDColors

0

Beyond Appearance: A Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks

F. Wang, Xiuyu Sun, Haowen Luo, Weihua Chen, Xianzhe Xu, Jian Jia, Yaohua Wang, Rong Jin•Wed Mar 29 2023

This paper aims to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent using a Semantic cOntrollable seLf-supervIseD lEaRning framework.

146 0

Exploring Visual Context for Weakly Supervised Person Search

Bingbing Ni, Ling Shao, Shengcai Liao, Yichao Yan, Xiaokang Yang, Jie Qin, Jinpeng Li•Fri Jun 18 2021

This paper inventively considers weakly supervised person search with only bounding box annotations, and discovers three levels of context clues in unconstrained natural images that promote local and global discriminative capabilities and enhances clustering accuracy.

40 0

Joint Detection and Identification Feature Learning for Person Search

Xiaogang Wang, Tong Xiao, Liang Lin, Shuang Li, Bochao Wang•Wed Apr 06 2016

A new deep learning framework for person search that jointly handles pedestrian detection and person re-identification in a single convolutional neural network and converges much faster and better than the conventional Softmax loss.

880 0

ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language

Yezhou Yang, Zhiyuan Fang, Jun Wang•Thu May 14 2020

The Visual-Textual Attribute Alignment model (dubbed as ViTAA) learns to disentangle the feature space of a person into subspaces corresponding to attributes using a light auxiliary attribute segmentation computing branch, and aligns these visual features with the textual attributes parsed from the sentences by using a novel contrastive learning loss.

216 0

Person Search in Videos with One Portrait Through Visual and Temporal Links

Dahua Lin, Wentao Liu, Qingqiu Huang•Thu Jul 26 2018

A novel framework is proposed, which takes into account the identity invariance along a tracklet, thus allowing person identities to be propagated via both the visual and the temporal links and remarkably outperforms mainstream person re-id methods.

66 0

Person Search with Natural Language Description

Xiaogang Wang, Bolei Zhou, Tong Xiao, Hongsheng Li, Shuang Li, Dayu Yue•Sat Feb 18 2017

An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search and a large-scale person description dataset with detailed natural language annotations and person samples from various sources is collected.

508 0

Query-Guided End-To-End Person Search

Federico Tombari, S. Amin, Fabio Galasso, Bharti Munjal•Thu May 02 2019

A novel query-guided end-to-end person search network (QEEPS) to address both person detection and re-identification and outperform the previous state-of-the-art datasets by a large margin.

143 0

advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns

Zhibo Wang, Qian Wang, H. Qi, Mengkai Song, Alireza Rahimpour•Sat Aug 24 2019

A novel attack algorithm, called advPattern, for generating adversarial patterns on clothes, which learns the variations of image pairs across cameras to pull closer the image features from the same camera, while pushing features from different cameras farther, demonstrates that deep re-ID systems are vulnerable to physical attacks.

66 0

Adding a benchmark result helps the community track progress.

Person Search | State-of-the-Art