Datasets
This is a resouce list for low light image enhancement
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
Rain Rendering for Evaluating and Improving Robustness to Bad Weather (Tremblay et al., 2020) (S. S. Halder et al., 2019)
Corruption and Perturbation Robustness (ICLR 2019)
【CVPR 2021, Variational Rain Generator】 From Rain Generation to Rain Removal
🌕 [ICCV 2021] Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection. A self-supervised learning way for low-light image object detection.
AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
Official Implementation of 'Fast AutoAugment' in PyTorch.
An implementation of paper "Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning" (CVPR19)
This repo contains the projects: 'Virtual Normal', 'DiverseDepth', and '3D Scene Shape'. They aim to solve the monocular depth estimation, 3D scene reconstruction from single image problems.
Code for AAAl 2024 paper: Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects
Towards Simulating Foggy and Hazy Images and Evaluating their Authenticity
ActMAD: Activation Matching to Align Distributions for Test-Time-Training (CVPR 2023)
A curated list of awesome dehazing papers
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
PyTorch Implementation of Data Augmentation GAN (originally proposed in arXiv:1711.04340)
This is the official implementation of TrivialAugment and a mini-library for the application of multiple image augmentation strategies including RandAugment and TrivialAugment.
[ECCV 2020] DADA: Differentiable Automatic Data Augmentation
[CVPR 2024] Domain Gap Embeddings for Generative Dataset Augmentation
Official PyTorch implementation of DiffuseMix : Label-Preserving Data Augmentation with Diffusion Models (CVPR'2024)
Official Implementation of WACV 2024 paper "Data Augmentation for Object Detection via Controllable Diffusion Models"
[CVPR 2024] U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
[CVPR 2024 Highlight] Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
TPSeNCE for image rain generation, deraining, and object detection.
Learning to See in the Dark. CVPR 2018