Person Retrieval

9 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)

huanghoujing/person-reid-triplet-loss-baseline ECCV 2018

RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency.

Dual-Path Convolutional Image-Text Embeddings with Instance Loss

layumi/Image-Text-Embedding 15 Nov 2017

In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.

Generalizing A Person Retrieval Model Hetero- and Homogeneously

zhunzhong07/HHL ECCV 2018

Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras.

PeR-ViS: Person Retrieval in Video Surveillance using Semantic Description

parshwa1999/PeR-ViS 4 Dec 2020

Instead of using an image query, in this paper, we study the problem of person retrieval in video surveillance with a semantic description.

APES: Audiovisual Person Search in Untrimmed Video

fuankarion/audiovisual-person-search 3 Jun 2021

To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval.

HAT: Hierarchical Aggregation Transformers for Person Re-identification

AI-Zhpp/HAT 13 Jul 2021

In this work, we take advantages of both CNNs and Transformers, and propose a novel learning framework named Hierarchical Aggregation Transformer (HAT) for image-based person Re-ID with high performance.

DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval

njtechcvlab/rstpreid-dataset 12 Sep 2021

Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality.

Part-based Pseudo Label Refinement for Unsupervised Person Re-identification

yoonkicho/pplr CVPR 2022

In this paper, we propose a novel Part-based Pseudo Label Refinement (PPLR) framework that reduces the label noise by employing the complementary relationship between global and part features.