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

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Overview

Text Based Person Search with Limited Data

PWC

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.

    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!

    photo_2022-08-07_18-44-28

    opened by ram7772 6
  • Cannot find test_query and train_query folders

    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

    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.

    image

    opened by ram7772 4
  • Problem about the clip_vocab_vit.npy

    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

    There is only caption_all.json in the dataset CUHK-PEDES, what are the train.json and test.json in the dataset part

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    opened by SwimKY 1
Releases(v0.1.1)
Owner
Xiao Han
Ph.D. student @ UoSurrey CVSSP, B.Eng. @ ZJU ISEE
Xiao Han
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