Code for our CVPR 2021 paper "MetaCam+DSCE"

Overview

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification (CVPR'21)

Introduction

Code for our CVPR 2021 paper "MetaCam+DSCE".

Prerequisites

  • CUDA>=10.0

  • At least two 1080-Ti GPUs

  • Other necessary packages listed in requirements.txt

  • Training Data

    (Market-1501, DukeMTMC-reID and MSMT-17. You can download these datasets from Zhong's repo)

    Unzip all datasets and ensure the file structure is as follow:

    MetaCam_DSCE/data    
    │
    └───market1501 OR dukemtmc OR msmt17
         │   
         └───DukeMTMC-reID OR Market-1501-v15.09.15 OR MSMT17_V1
             │   
             └───bounding_box_train
             │   
             └───bounding_box_test
             | 
             └───query
             │   
             └───list_train.txt (only for MSMT-17)
             | 
             └───list_query.txt (only for MSMT-17)
             | 
             └───list_gallery.txt (only for MSMT-17)
             | 
             └───list_val.txt (only for MSMT-17)
    

Usage

See run.sh for details.

Acknowledgments

This repo borrows partially from MWNet (meta-learning), ECN (exemplar memory) and SpCL (faiss-based acceleration). If you find our code useful, please cite their papers.

@inproceedings{shu2019meta,
  title={Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting},
  author={Shu, Jun and Xie, Qi and Yi, Lixuan and Zhao, Qian and Zhou, Sanping and Xu, Zongben and Meng, Deyu},
  booktitle={NeurIPS},
  year={2019}
}
@inproceedings{zhong2019invariance,
  title={Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification},
  author={Zhong, Zhun and Zheng, Liang and Luo, Zhiming and Li, Shaozi and Yang, Yi},
  booktitle={CVPR},
  year={2019},
}
@inproceedings{ge2020selfpaced,
    title={Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID},
    author={Yixiao Ge and Feng Zhu and Dapeng Chen and Rui Zhao and Hongsheng Li},
    booktitle={NeurIPS},
    year={2020}
}

Citation

@inproceedings{yang2021meta,
  title={Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification},
  author={Yang, Fengxiang and Zhong, Zhun and Luo, Zhiming and Cai, Yuanzheng and Li, Shaozi and Nicu, Sebe},
  booktitle={CVPR},
  year={2021},
}

Resources

  1. Pre-trained MMT-500 models to reproduce Tab. 3 of our paper. BaiduNetDisk, Passwd: nsbv. Google Drive.

  2. Pedestrian images used to plot Fig.3 in our paper. BaiduNetDisk, Passwd: ydrf. Google Drive.

    Please download 'marCam' and 'dukeCam', put them under 'MetaCam_DSCE/data' and uncomment corresponding code. (e.g., L#87-89, L#163-168 of train_usl_knn_merge.py)

Contact Us

Email: [email protected]

Owner
FlyingRoastDuck
FlyingRoastDuck
Neural Re-rendering for Full-frame Video Stabilization

NeRViS: Neural Re-rendering for Full-frame Video Stabilization Project Page | Video | Paper | Google Colab Setup Setup environment for [Yu and Ramamoo

Yu-Lun Liu 9 Jun 17, 2022
Source code for "Roto-translated Local Coordinate Framesfor Interacting Dynamical Systems"

Roto-translated Local Coordinate Frames for Interacting Dynamical Systems Source code for Roto-translated Local Coordinate Frames for Interacting Dyna

Miltiadis Kofinas 19 Nov 27, 2022
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
Internship Assessment Task for BaggageAI.

BaggageAI Internship Task Problem Statement: You are given two sets of images:- background and threat objects. Background images are the background x-

Arya Shah 10 Nov 14, 2022
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 02, 2023
NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch

PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt

Vaibhav Balloli 320 Jan 02, 2023
Emotional conditioned music generation using transformer-based model.

This is the official repository of EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation. The paper has b

hung anna 96 Nov 09, 2022
Automatic voice-synthetised summaries of latest research papers on arXiv

PaperWhisperer PaperWhisperer is a Python application that keeps you up-to-date with research papers. How? It retrieves the latest articles from arXiv

Valerio Velardo 124 Dec 20, 2022
This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning"

CSP_Deep_EEG This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning" {https://www

Seyed Mahdi Roostaiyan 2 Nov 08, 2022
A simple, fast, and efficient object detector without FPN

You Only Look One-level Feature (YOLOF), CVPR2021 A simple, fast, and efficient object detector without FPN. This repo provides an implementation for

789 Jan 09, 2023
High frequency AI based algorithmic trading module.

Flow Flow is a high frequency algorithmic trading module that uses machine learning to self regulate and self optimize for maximum return. The current

59 Dec 14, 2022
Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

Daniel Povey 41 Jan 07, 2023
Train an imgs.ai model on your own dataset

imgs.ai is a fast, dataset-agnostic, deep visual search engine for digital art history based on neural network embeddings.

Fabian Offert 5 Dec 21, 2021
Robot Reinforcement Learning on the Constraint Manifold

Implementation of "Robot Reinforcement Learning on the Constraint Manifold"

31 Dec 05, 2022
The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc

Miaomiao Li 82 Jan 02, 2023
An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches

Transformer-in-Transformer An Implementation of the Transformer in Transformer paper by Han et al. for image classification, attention inside local pa

Rishit Dagli 40 Jul 25, 2022
Membership Inference Attack against Graph Neural Networks

MIA GNN Project Starter If you meet the version mismatch error for Lasagne library, please use following command to upgrade Lasagne library. pip insta

6 Nov 09, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

Overcooked-AI We suppose to apply traditional offline reinforcement learning technique to multi-agent algorithm. In this repository, we implemented be

Baek In-Chang 14 Sep 16, 2022
PoolFormer: MetaFormer is Actually What You Need for Vision

PoolFormer: MetaFormer is Actually What You Need for Vision (arXiv) This is a PyTorch implementation of PoolFormer proposed by our paper "MetaFormer i

Sea AI Lab 1k Dec 30, 2022