Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Related tags

Deep LearningCycleContrast
Overview

Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

This is a official implementation of the CycleContrast introduced in the paper:Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Citation

If you find our work useful, please cite:

@article{wu2021contrastive,
  title={Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency},
  author={Wu, Haiping and Wang, Xiaolong},
  journal={arXiv preprint arXiv:2105.06463},
  year={2021}
}

Preparation

Our code is tested on Python 3.7 and Pytorch 1.3.0, please install the environment via

pip install -r requirements.txt

Model Zoo

We provide the model pretrained on R2V2 for 200 epochs.

method pre-train epochs on R2V2 dataset ImageNet Top-1 Linear Eval OTB Precision OTB Success UCF Top-1 pretrained model
MoCo 200 53.8 56.1 40.6 80.5 pretrain ckpt
CycleContrast 200 55.7 69.6 50.4 82.8 pretrain ckpt

Run Experiments

Data preparation

Download R2V2 (Random Related Video Views) dataset according to https://github.com/danielgordon10/vince.

The direction structure should be as followed:

CycleContrast
├── cycle_contrast 
├── scripts 
├── utils 
├── data
│   ├── r2v2_large_with_ids 
│   │   ├── train 
│   │   │   ├── --/
│   │   │   ├── -_/
│   │   │   ├── _-/
│   │   │   ├── __/
│   │   │   ├── -0/
│   │   │   ├── _0/
│   │   │   ├── ...
│   │   │   ├── zZ/
│   │   │   ├── zz/
│   │   ├── val
│   │   │   ├── --/
│   │   │   ├── -_/
│   │   │   ├── _-/
│   │   │   ├── __/
│   │   │   ├── -0/
│   │   │   ├── _0/
│   │   │   ├── ...
│   │   │   ├── zZ/
│   │   │   ├── zz/

Unsupervised Pretrain

./scripts/train_cycle.sh

Downstream task - ImageNet linear eval

Prepare ImageNet dataset according to pytorch ImageNet training code.

MODEL_DIR=output/cycle_res50_r2v2_ep200
IMAGENET_DATA=data/ILSVRC/Data/CLS-LOC
./scripts/eval_ImageNet.sh $MODEL_DIR $IMAGENET_DATA

Downstream task - OTB tracking

Transfer to OTB tracking evaluation is based on SiamFC-Pytorch. Please prepare environment and data according to SiamFC-Pytorch

git clone https://github.com/happywu/mmaction2-CycleContrast
# path to your pretrained model, change accordingly
CycleContrast=/home/user/code/CycleContrast
PRETRAIN=${CycleContrast}/output/cycle_res50_r2v2_ep200/checkpoint_0199.pth.tar
cd mmaction2_tracking
./scripts/submit_r2v2_r50_cycle.py ${PRETRAIN}

Downstream task - UCF classification

Transfer to UCF action recognition evaluation is based on AVID-CMA, prepare data and env according to AVID-CMA.

git clone https://github.com/happywu/AVID-CMA-CycleContrast
# path to your pretrained model, change accordingly
CycleContrast=/home/user/code/CycleContrast
PRETRAIN=${CycleContrast}/output/cycle_res50_r2v2_ep200/checkpoint_0199.pth.tar
cd AVID-CMA-CycleContrast 
./scripts/submit_r2v2_r50_cycle.py ${PRETRAIN}

Acknowledgements

The codebase is based on FAIR-MoCo. The OTB tracking evaluation is based on MMAction2, SiamFC-PyTorch and vince. The UCF classification evaluation follows AVID-CMA.

Thank you all for the great open source repositories!

You might also like...
[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations
[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

CrowdNav with Social-NCE This is an official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations by

VITA lab at EPFL 125 Dec 23, 2022
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

Thalles Silva 1.7k Dec 28, 2022
Supervised Contrastive Learning for Downstream Optimized Sequence Representations
Supervised Contrastive Learning for Downstream Optimized Sequence Representations

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

Hooman Sedghamiz 18 Oct 21, 2022
《LXMERT: Learning Cross-Modality Encoder Representations from Transformers》(EMNLP 2020)

The Most Important Thing. Our code is developed based on: LXMERT: Learning Cross-Modality Encoder Representations from Transformers

null 53 Dec 16, 2022
SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING - The Facebook paper about fine tuning RoBERTa with contrastive loss
SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING - The Facebook paper about fine tuning RoBERTa with contrastive loss

"# SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING" i

Saeed Lotfi 28 Dec 12, 2022
Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence
Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training process.

Yang Wenhan 44 Dec 6, 2022
Cross Quality LFW: A database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

Cross-Quality Labeled Faces in the Wild (XQLFW) Here, we release the database, evaluation protocol and code for the following paper: Cross Quality LFW

Martin Knoche 10 Dec 12, 2022
Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Pixel-Level Cycle Association This is the Pytorch implementation of our NeurIPS 2020 Oral paper Pixel-Level Cycle Association: A New Perspective for D

null 87 Oct 19, 2022
Code and models for ICCV2021 paper
Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Robust Object Detection via Instance-Level Temporal Cycle Confusion This repo contains the implementation of the ICCV 2021 paper, Robust Object Detect

Xin Wang 69 Oct 13, 2022
Owner
GitHub Repository
Jremesh-tools - Blender addon for quad remeshing

JRemesh Tools Blender 2.8 - 3.x addon for quad remeshing. Currently it is a wrap

Jayanam 89 Dec 30, 2022
Continual Learning of Long Topic Sequences in Neural Information Retrieval

ContinualPassageRanking Repository for the paper "Continual Learning of Long Topic Sequences in Neural Information Retrieval". In this repository you

0 Apr 12, 2022
Vision Deep-Learning using Tensorflow, Keras.

Welcome! I am a computer vision deep learning developer working in Korea. This is my blog, and you can see everything I've studied here. https://www.n

kimminjun 6 Dec 14, 2022
Dataset and codebase for NeurIPS 2021 paper: Exploring Forensic Dental Identification with Deep Learning

Repository under construction. Example dataset, checkpoints, and training/testing scripts will be avaible soon! 💡 Collated best practices from most p

4 Jun 26, 2022
A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

Neuraxle Pipelines Code Machine Learning Pipelines - The Right Way. Neuraxle is a Machine Learning (ML) library for building machine learning pipeline

Neuraxio 555 Dec 24, 2022
Project ArXiv Citation Network

Project ArXiv Citation Network Overview This project involved the analysis of the ArXiv citation network. Usage The complete code of this project is i

Dennis Núñez-Fernández 5 Oct 20, 2022
Convert openmmlab (not only mmdetection) series model to tensorrt

MMDet to TensorRT This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is exp

JinTian 4 Dec 17, 2021
Novel Instances Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection

Novel Instances Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection (NimPme) The official implementation of Novel Instances Mining with

12 Sep 08, 2022
This is the code related to "Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation" (ICCV 2021).

Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation This is the code relat

39 Sep 23, 2022
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,

GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan 70 Dec 18, 2022
Activity image-based video retrieval

Cross-modal-retrieval Our approach is focus on Activity Image-to-Video Retrieval (AIVR) task. The compared methods are state-of-the-art single modalit

BCMI 75 Oct 21, 2021
Deep Reinforcement Learning for Multiplayer Online Battle Arena

MOBA_RL Deep Reinforcement Learning for Multiplayer Online Battle Arena Prerequisite Python 3 gym-derk Tensorflow 2.4.1 Dotaservice of TimZaman Seed R

Dohyeong Kim 32 Dec 18, 2022
A convolutional recurrent neural network for classifying A/B phases in EEG signals recorded for sleep analysis.

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

Apurva R. Umredkar 2 Nov 25, 2022
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Minqi 297 Dec 12, 2022
The Deep Learning with Julia book, using Flux.jl.

Deep Learning with Julia DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the

Logan Kilpatrick 67 Dec 25, 2022
Taming Transformers for High-Resolution Image Synthesis

Taming Transformers for High-Resolution Image Synthesis CVPR 2021 (Oral) Taming Transformers for High-Resolution Image Synthesis Patrick Esser*, Robin

CompVis Heidelberg 3.5k Jan 03, 2023
KaziText is a tool for modelling common human errors.

KaziText KaziText is a tool for modelling common human errors. It estimates probabilities of individual error types (so called aspects) from grammatic

ÚFAL 3 Nov 24, 2022
A keras-based real-time model for medical image segmentation (CFPNet-M)

CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation This repository contains the implementat

268 Nov 27, 2022
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

DIKSHA DESWAL 1 Dec 29, 2021
Implementation of the "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos" paper.

Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos Introduction Point cloud videos exhibit irregularities and lack of or

Hehe Fan 101 Dec 29, 2022