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
Optimized primitives for collective multi-GPU communication

NCCL Optimized primitives for inter-GPU communication. Introduction NCCL (pronounced "Nickel") is a stand-alone library of standard communication rout

NVIDIA Corporation 2k Jan 09, 2023
Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Jimmy Wu 27 Nov 30, 2022
PyTorch code for DriveGAN: Towards a Controllable High-Quality Neural Simulation

PyTorch code for DriveGAN: Towards a Controllable High-Quality Neural Simulation

76 Dec 24, 2022
Re-implement CycleGAN in Tensorlayer

CycleGAN_Tensorlayer Re-implement CycleGAN in TensorLayer Original CycleGAN Improved CycleGAN with resize-convolution Prerequisites: TensorLayer Tenso

89 Aug 15, 2022
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

Hyunwoo Kim 51 Jan 06, 2023
Self-training for Few-shot Transfer Across Extreme Task Differences

Self-training for Few-shot Transfer Across Extreme Task Differences (STARTUP) Introduction This repo contains the official implementation of the follo

Cheng Perng Phoo 33 Oct 31, 2022
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

Instance-Aware Latent-Space Search This is a PyTorch implementation of the following paper: Disentangled Face Attribute Editing via Instance-Aware Lat

67 Dec 21, 2022
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
Code for EMNLP2020 long paper: BERT-Attack: Adversarial Attack Against BERT Using BERT

BERT-ATTACK Code for our EMNLP2020 long paper: BERT-ATTACK: Adversarial Attack Against BERT Using BERT Dependencies Python 3.7 PyTorch 1.4.0 transform

Linyang Li 142 Jan 04, 2023
It's final year project of Diploma Engineering. This project is based on Computer Vision.

Face-Recognition-Based-Attendance-System It's final year project of Diploma Engineering. This project is based on Computer Vision. Brief idea about ou

Neel 10 Nov 02, 2022
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection

LiDAR Distillation Paper | Model LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection Yi Wei, Zibu Wei, Yongming Rao, Jiax

Yi Wei 75 Dec 22, 2022
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh

Akshita Gupta 127 Dec 27, 2022
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)

Wenwen Yu 498 Dec 24, 2022
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar

Octavian Ganea 154 Jan 02, 2023
An implementation of a sequence to sequence neural network using an encoder-decoder

Keras implementation of a sequence to sequence model for time series prediction using an encoder-decoder architecture. I created this post to share a

Luke Tonin 195 Dec 17, 2022
PyG (PyTorch Geometric) - A library built upon PyTorch to easily write and train Graph Neural Networks (GNNs)

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

PyG 16.5k Jan 08, 2023
An open source object detection toolbox based on PyTorch

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

Bo Chen 24 Dec 28, 2022
This is the repository for paper NEEDLE: Towards Non-invertible Backdoor Attack to Deep Learning Models.

This is the repository for paper NEEDLE: Towards Non-invertible Backdoor Attack to Deep Learning Models.

1 Oct 25, 2021
U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

U-Net Implementation By Christopher Ley This is my interpretation and implementation of the famous paper "U-Net: Convolutional Networks for Biomedical

Christopher Ley 1 Jan 06, 2022
Code for our CVPR 2022 Paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection"

GEN-VLKT Code for our CVPR 2022 paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection". Contributed by Yue Lia

Yue Liao 47 Dec 04, 2022