An official TensorFlow implementation of “CLCC: Contrastive Learning for Color Constancy” accepted at CVPR 2021.

Yi-Chen (Howard) Lo

Last update: Dec 17, 2022

Related tags

Deep Learning mediatek deep-learning white-balance color-checker illuminant-estimation color-constancy contrastive-learning cvpr2021

Overview

CLCC: Contrastive Learning for Color Constancy (CVPR 2021)

Yi-Chen Lo*, Chia-Che Chang*, Hsuan-Chao Chiu, Yu-Hao Huang, Chia-Ping Chen, Yu-Lin Chang, Kevin Jou

MediaTek Inc., Hsinchu, Taiwan

(*) indicates equal contribution.

Paper | Poster | 5-min Video | 5-min Slides | 10-min Slides

Dataset

We preprocess each fold of dataset and stored in .pkl format for each sample. Each sample contains:

Raw image: Mask color checker; Subtract black level; Convert to uint16 [0, 65535] BGR numpy array with shape (H, W, 3).
RGB label: L2-normalized numpy vector with shape (3,).
Color checker: [0, 4095] BGR numpy array with shape (24, 3) for raw-to-raw mapping presented in our paper (see util/raw2raw.py and also section 4.3 in our paper). A few of them are stored in all zeros due to the failure of color checker detection. Note that we convert it into RGB format during preprocessing in dataloader.py, and our raw-to-raw mapping algorithm also manipulates it in RGB format.

Training and Evaluation

CLCC is a Python 3 & TensorFlow 1.x implementation based on FC4 codebase.

Dataset preparation: Download preprocessed dataset here. Please make sure your dataset folder is structured as <DATA_DIR>/<DATA_NAME>/<FOLD_ID> (e.g., data/gehler/0, just like how it is structured in download source).
Pretrained weights preparation: Download ImageNet-pretrained weights here. Place pretrained weight files under pretrained_models/imagenet/.
Training: Modify config.py (i.e., you may want to rename EXP_NAME and specify training data DATA_NAME, TRAIN_FOLDS, TEST_FOLDS) and execute train.py. Checkpoints will be saved under ckpts/EXP_NAME during training.
Evaluation: Once training is done, you can evaluate checkpoint with eval.py on a specific test fold. We recommend to refer to scripts/eval_squeezenet_clcc_gehler.sh for 3-fold cross-validation.

Acknowledgments

FC4: https://github.com/yuanming-hu/fc4.
Color checker detection: https://github.com/colour-science/colour-checker-detection. To increase detection accuracy, performing homography with color checker coordinates provided by the original dataset can help a lot.

Citation

@InProceedings{Lo_2021_CVPR,
    author    = {Lo, Yi-Chen and Chang, Chia-Che and Chiu, Hsuan-Chao and Huang, Yu-Hao and Chen, Chia-Ping and Chang, Yu-Lin and Jou, Kevin},
    title     = {CLCC: Contrastive Learning for Color Constancy},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8053-8063}
}

Comments

Maybe a mistake at raw2raw#L92 ？

code at https://github.com/howardyclo/clcc-cvpr21/blob/master/util/raw2raw.py#L92

M_AC = (1 - w[:,None,None]) * I + w[:,None,None] * M_AB
M_BC = (1 - w[:,None,None]) * I + w[:,None,None] * M_BA

if Ill_c be defined by M_AC ，then M_BC should be M_BC = (1 - w[:,None,None]) * M_BA + w[:,None,None] *I

prove as below:

M_BC  = M_BA @ M_AC = M_BA @ ( (1 - w[:,None,None]) * I + w[:,None,None] * M_AB )
## ==>
M_BC  = ( (1 - w[:,None,None]) * M_BA  + w[:,None,None] * M_BA @M_AB )
## M_BA @M_AB closed to I theoretically.
M_BC = (1 - w[:,None,None]) * M_BA + w[:,None,None] *I

opened by DavideHe 0

This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis, accepted at EMNLP 2021.

MultiModal-InfoMax This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Informa

Deep Cognition and Language Research (DeCLaRe) Lab

89 Dec 26, 2022

Lipstick ain't enough: Beyond Color-Matching for In-the-Wild Makeup Transfer (CVPR 2021)

Table of Content Introduction Datasets Getting Started Requirements Usage Example Training & Evaluation CPM: Color-Pattern Makeup Transfer CPM is a ho

248 Dec 13, 2022

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

2021-CVPR-MvCLN This repo contains the code and data of the following paper accepted by CVPR 2021 Partially View-aligned Representation Learning with

33 Nov 1, 2022

Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021.

Dense Contrastive Learning for Self-Supervised Visual Pre-Training This project hosts the code for implementing the DenseCL algorithm for se

491 Jan 3, 2023

Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

Geometric Vector Perceptron Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biom

59 Nov 24, 2022

PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Completer: Incomplete Multi-view Clustering via Contrastive Prediction This repo contains the code and data of the following paper accepted by CVPR 20

72 Dec 7, 2022

Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

17 Jan 1, 2023

This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

NeurIPS 2021 (Spotlight): Task-Adaptive Neural Network Search with Meta-Contrastive Learning This is an official PyTorch implementation of Task-Adapti

15 Nov 21, 2022

This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021.

PyTorch implementation of DAQ This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021. For more informatio

36 Nov 4, 2022

An official TensorFlow implementation of “CLCC: Contrastive Learning for Color Constancy” accepted at CVPR 2021.

Related tags

Overview

CLCC: Contrastive Learning for Color Constancy (CVPR 2021)

Paper | Poster | 5-min Video | 5-min Slides | 10-min Slides

Dataset

Training and Evaluation

Acknowledgments

Citation

You might also like...

This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis, accepted at EMNLP 2021.

Lipstick ain't enough: Beyond Color-Matching for In-the-Wild Makeup Transfer (CVPR 2021)

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021.

Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021.

Comments

Maybe a mistake at raw2raw#L92 ？

Owner

Yi-Chen (Howard) Lo

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

This is the official implementation code repository of Underwater Light Field Retention : Neural Rendering for Underwater Imaging (Accepted by CVPR Workshop2022 NTIRE)

Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

This repository provides the official implementation of 'Learning to ignore: rethinking attention in CNNs' accepted in BMVC 2021.

The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN", accepted to ACM MM 2021 BNI Track.