Codebase for BMVC 2021 paper "Text Based Person Search with Limited Data"

Last update: Nov 24, 2022

Related tags

Deep Learning TextReID

Overview

Text Based Person Search with Limited Data

This is the codebase for our BMVC 2021 paper.

Please bear with me refactoring this codebase after CVPR deadline 😅

Abstract

Text-based person search (TBPS) aims at retrieving a target person from an image gallery with a descriptive text query. Solving such a fine-grained cross-modal retrieval task is challenging, which is further hampered by the lack of large-scale datasets. In this paper, we present a framework with two novel components to handle the problems brought by limited data. Firstly, to fully utilize the existing small-scale benchmarking datasets for more discriminative feature learning, we introduce a cross-modal momentum contrastive learning framework to enrich the training data for a given mini-batch. Secondly, we propose to transfer knowledge learned from existing coarse-grained large-scale datasets containing image-text pairs from drastically different problem domains to compensate for the lack of TBPS training data. A transfer learning method is designed so that useful information can be transferred despite the large domain gap. Armed with these components, our method achieves new state of the art on the CUHK-PEDES dataset with significant improvements over the prior art in terms of Rank-1 and mAP.

Comments

Research prepared to obtain a diploma degree in computer and Automation Engineering.

Hello!

My research focuses on Person search using Visual-Textual Attributes. Having said that, I would like to use your model to assist me in my project, but I have some issues when I finish train and test the model. My problem is trying to write code to run the model to get the same response as the photo. so Can you help me please!

opened by ram7772 6
Cannot find test_query and train_query folders
Hi @BrandonHanx

In the ReadMe file, it is mentioned to setup the datasets dir as follows:

└── cuhkpedes ├── annotations │ ├── test.json │ ├── train.json │ └── val.json ├── clip_vocab_vit.npy └── imgs ├── cam_a ├── cam_b ├── CUHK01 ├── CUHK03 ├── Market ├── test_query └── train_query

After downloading the cuhkpedes data set, we get only the imgs folder, containing cam_a, cam_b and CUHK01 folders. there is no test_query and train_query folders. Also, these folders are not in the repository. Could you provide more information regarding on these folders, more exactly, what kind of information they contain and how they must be set up?

Also, there are few more folders that are not part of the cuhkpedes, such as CUHK03 and Market. Do we need these data sets to reproduce the results?

Best regards, liviust
opened by liviust 5
some problem in training and testing

Hello

I have some problem. first: I don't find test_query and train_query file when I get images from [Dr. Shuang Li] second: I have this problem for testing and training.

opened by ram7772 4
Problem about the clip_vocab_vit.npy

Hi :) I have a question about the pre-processing document clip_vocab_vit.npy. My understanding is that it contains the tensor of the CLIP-Text-Encoder output corresponding to each word (total 9408). My question is, the output dimension of CLIP-TEXT-ENCODER is 1024, but the tensor dimension of each word in clip_vocab_vit.npy is 512. Is there some other operation in it? Thanks

opened by Frost-Yang-99 2
There is only caption_all.json in the dataset CUHK-PEDES, what are the train.json and test.json in the dataset part
Describe the bug A clear and concise description of what the bug is.

To Reproduce Steps to reproduce the behavior:

Go to '...'

Click on '....'

Scroll down to '....'

See error

Expected behavior A clear and concise description of what you expected to happen.

Screenshots If applicable, add screenshots to help explain your problem.

Desktop (please complete the following information):

OS: [e.g. iOS]

Browser [e.g. chrome, safari]

Version [e.g. 22]

Smartphone (please complete the following information):

Device: [e.g. iPhone6]

OS: [e.g. iOS8.1]

Browser [e.g. stock browser, safari]

Version [e.g. 22]

Additional context Add any other context about the problem here.
opened by SwimKY 1

Releases(v0.1.1)

v0.1.1(Dec 10, 2021)

Full Changelog: https://github.com/BrandonHanx/TextReID/compare/v0.1.0...v0.1.1
Source code(tar.gz)
Source code(zip)

Owner

Xiao Han

Ph.D. student @ UoSurrey CVSSP, B.Eng. @ ZJU ISEE

GitHub Repository

offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

76 Dec 08, 2022

The source code of "SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation", accepted to WACV 2022.

SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation The source code of our work "SIDE: Center-based Stereo 3D Detecto

10 Dec 18, 2022

Unsupervised Discovery of Object Radiance Fields

Unsupervised Discovery of Object Radiance Fields by Hong-Xing Yu, Leonidas J. Guibas and Jiajun Wu from Stanford University. arXiv link: https://arxiv

148 Nov 30, 2022

This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on table detection and table structure recognition.

WTW-Dataset This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on ICCV 2021. Here, you can download the

109 Dec 29, 2022

Nonnegative spatial factorization for multivariate count data

Nonnegative spatial factorization for multivariate count data This repository contains supporting code to facilitate reproducible analysis. For detail

24 Dec 19, 2022

PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

112 Nov 28, 2022

Crossover Learning for Fast Online Video Instance Segmentation (ICCV 2021)

TL;DR: CrossVIS (Crossover Learning for Fast Online Video Instance Segmentation) proposes a novel crossover learning paradigm to fully leverage rich c

79 Nov 25, 2022

PyTorch Lightning + Hydra. A feature-rich template for rapid, scalable and reproducible ML experimentation with best practices. ⚡🔥⚡

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

2.1k Jan 09, 2023

Procedural 3D data generation pipeline for architecture

Synthetic Dataset Generator Authors: Stanislava Fedorova Alberto Tono Meher Shashwat Nigam Jiayao Zhang Amirhossein Ahmadnia Cecilia bolognesi Dominik

49 Nov 25, 2022

An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022

Probabilistic Cross-Modal Embedding (PCME) CVPR 2021

Probabilistic Cross-Modal Embedding (PCME) CVPR 2021 Official Pytorch implementation of PCME | Paper Sanghyuk Chun1 Seong Joon Oh1 Rafael Sampaio de R

87 Dec 21, 2022

Lava-DL, but with PyTorch-Lightning flavour

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

4 Oct 31, 2022

A transformer which can randomly augment VOC format dataset (both image and bbox) online.

VocAug It is difficult to find a script which can augment VOC-format dataset, especially the bbox. Or find a script needs complex requirements so it i

1 Mar 05, 2022

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

6.5k Jan 04, 2023

CPU inference engine that delivers unprecedented performance for sparse models

The DeepSparse Engine is a CPU runtime that delivers unprecedented performance by taking advantage of natural sparsity within neural networks to reduce compute required as well as accelerate memory b

1.2k Jan 09, 2023

[SIGGRAPH'22] StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

[Project] [PDF] This repository contains code for our SIGGRAPH'22 paper "StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets" by Axel Sauer, Katja

742 Jan 04, 2023

[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

125 Dec 23, 2022

Official Implementation of PCT

Official Implementation of PCT Prerequisites python == 3.8.5 Please make sure you have the following libraries installed: numpy torch=1.4.0 torchvisi

32 Nov 21, 2022

NasirKhusraw - The TSP solved using genetic algorithm and show TSP path overlaid on a map of the Iran provinces & their capitals.

Nasir Khusraw : Travelling Salesman Problem The TSP solved using genetic algorithm. This project show TSP path overlaid on a map of the Iran provinces

2 Sep 01, 2022

Object Detection Projekt in GKI WS2021/22

tfObjectDetection Object Detection Projekt with tensorflow in GKI WS2021/22 Docker Container: docker run -it --name --gpus all -v path/to/project:p

1 Jul 18, 2022