Name: VIML_CVDL: Computer Vision and Deep Learning
Type: Organization
Bio: We are Vision & Interactive Media Laboratory (VIML), Computer Vision and Deep Learning (CVDL) Group, located at the National University of Singapore.
Location: National University of Singapore, Singapore
Blog: https://tanrobby.github.io/
VIML_CVDL: Computer Vision and Deep Learning's Projects
an awesome list of autonomous driving datasets
Collection of recent nighttime enhancement works, including papers, codes, datasets, and metrics.
This repository contains the code and models for the following paper.
Attentive Generative Adversarial Network for Raindrop Removal from A Single Image (CVPR 2018)
Robust optical flow estimation in dynamic weathers (e.g. rain, snow, sleet, ...)
[CVPR2023] EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
An implementation of paper "Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning" (CVPR19)
knowledge distillation papers
This is the official code for paper "Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI" [AAAI2024]
Implementation of paper "Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression" (ECCV22)
Repository containing a list of labelled/unlabelled nighttime datasets
[ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution"
Codes for the paper - Depth Estimation in Nighttime using Stereo-Consistent Cyclic Translations, arXiv'19
[ECCV2024] "Raindrop Clarity: A Dual-Focused Dataset for Day and Night Raindrop Removal", https://arxiv.org/abs/2407.16957
Code for Generating Rain Streaks for paper "Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network"
Benchmarks, Datasets, Papers
Unofficial PyTorch code for the paper - Deep Retinex Decomposition for Low-Light Enhancement, BMVC'18
AAAI22, Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning