61 Open source projects that are alternatives of or similar to CONTRIQUE

[unofficial] Pytorch implementation of WaDIQaM in TIP2018, Bosse S. et al. (Deep neural networks for no-reference and full-reference image quality assessment)

Code for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors".

python implementation of the paper "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" - cvpr 2017

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

This repository contains the code implementation used in the paper Temporally Coherent Embeddings for Self-Supervised Video Representation Learning (TCE).

Implementation of the paper "No Reference Image Quality Assessment in the Spatial Domain" by A Mittal et al. in OpenCV (using both C++ and Python)

Source code for "From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality"

PyTorch implementation of the InfoNCE loss for self-supervised learning.

Official PyTorch implementation of Contrastive Learning of Musical Representations

Official Code for Paper <XCloud: Design and Implementation of AI Cloud Platform with RESTful API Service> (arXiv1912.10344)

Parametric Contrastive Learning (ICCV2021)

Pan-sharpening in the Earth Engine code editor

[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

[WACV'22] Code repository for the paper "Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting", https://arxiv.org/abs/2106.10137.

AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries

[EMNLP 2021] Mirror-BERT: Converting Pretrained Language Models to universal text encoders without labels.

A resource list and performance benchmark for blind video quality assessment (BVQA) models on user-generated content (UGC) datasets. [IEEE TIP'2021] "UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content", Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik

PyTorch Implementation on Paper [CVPR2021]Distilling Audio-Visual Knowledge by Compositional Contrastive Learning

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]

The codebase for "Group-wise Contrastive Learning for Neural Dialogue Generation" (Cai et al., Findings of EMNLP 2020)

Code for the paper "Contrastive Learning Inverts the Data Generating Process".

[NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning

official implemntation for "Contrastive Learning with Stronger Augmentations"

Implementation for "3D human pose, shape and texture from low-resolution images and videos", TPAMI 2021

Code for the RA-L (IROS) 2021 paper "A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition"

[IEEE OJSP'2021] "RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content", Zhengzhong Tu, Xiangxu Yu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik

Generalized Deep Metric Learning.

Pytorch implementation of "A Simple Framework for Contrastive Learning of Visual Representations"

[NeurIPS 2021] COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

This is the code base for paper "G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling" by Souradip Chakraborty, Aritra Roy Gosthipaty and Sayak Paul.

CVC: Contrastive Learning for Non-parallel Voice Conversion (INTERSPEECH 2021, in PyTorch)

Code for EMNLP 2021 paper "Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization"

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Official PyTorch and MATLAB implementations of our MICCAI 2020 paper "FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology"

PyTorch implementation of SimCLR: supports multi-GPU training and closely reproduces results

CLIP (Contrastive Language–Image Pre-training) for Italian

An open source implementation of CLIP.

Model-Contrastive Federated Learning (CVPR 2021)

Code and resources on scalable and efficient Graph Neural Networks

Object-aware Contrastive Learning for Debiased Scene Representation (NeurIPS 2021)

A python implementation of BRISQUE Image Quality Assessment

Toolbox of commonly-used image quality assessment algorithms.

List of Publications in Graph Contrastive Learning

Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al.

Experiments with supervised contrastive learning methods with different loss functions

The Haar wavelet-based perceptual similarity index (HaarPSI) is a similarity measure for images that aims to correctly assess the perceptual similarity between two images with respect to a human viewer.

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

📄 Spatial Contrastive Learning for Few-Shot Classification (ECML/PKDD 2021).

A curated list of awesome graph & self-supervised-learning-based recommendation.

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

Crunch is a tool for lossy PNG image file optimization. It combines selective bit depth, color type, and color palette reduction with zopfli DEFLATE compression algorithm encoding using the pngquant and zopflipng PNG optimization tools. This approach leads to a significant file size gain relative to lossless approaches at the expense of a relatively modest decrease in image quality (see example images below).

Siamese and triplet networks with online pair/triplet mining in PyTorch

[official] Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment (ACM MM 2020)

Writer independent offline signature verification using convolutional siamese networks

Python code to compute features of classic Image Quality Assessment models

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

[CVPRW 2021] Codes for Region-Adaptive Deformable Network for Image Quality Assessment

Sensifai image and video enhancement module on mobiles

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.