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awesome-segment-anything

Tracking and collecting papers/projects/others related to Segment Anything.

baoliang93.github.io

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bert-whitening

简单的向量白化改善句向量质量

biqa_tools_for_customdataset

Test BIQA methods on my own dataset

dassl.pytorch

A PyTorch toolbox for domain adaptation and semi-supervised learning.

deepiqa

Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment

dfss-iqa

Code for "Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment".

dmm

dqa

fpr

Code for "No-Reference Image Quality Assessment by Hallucinating Pristine Features"

generalization-causality

一些domain generalization相关的阅读笔记

gstvqa

Code for 'Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment'

iaca_for_lowlight_iqa

Code for "Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement"

image-quality-assessment

IQA( FID, PSNR, FSIM, SSIM, MAE & FPM) indicator test code

image-quality-assessment-by-multiple-models

Using multiple models (CNN, LeNet-5, ResNet, VGG) to implement IQA and compare their performance

medsam

The official repository for MedSAM: Segment Anything in Medical Images.

ntire2021-iqa-macs

NTIRE 2021, Image Quality Assessment Challenge (MACS team)

pfr-nriqa

Code for “No-Reference Image Quality Assessment byLearning Pseudo Reference in Feature Level”

pre-trained-language-models-for-interactive-decision-making

Pre-Trained Language Models for Interactive Decision-Making [NeurIPS 2022]

pytorch-loss-functions

A collection of loss functions with easy usage

q-bench

A benchmark for multi-modality LLMs (MLLMs) on low-level vision and visual quality assessment.

sed-awesome-segment-anything

This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).

train_custom_dataset

标注自己的数据集，训练、评估、测试、部署自己的人工智能算法

transfer-learning-library

Transfer Learning Library for Domain Adaptation and Finetune.

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

tres

No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency

txt2csv

Get the numbers in each txt line to a CSV file.

uegan

[TIP2020] Pytorch implementation of "Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network"

umnn

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

visual-mic

Passive Recovery of Sound from Video