jinzhenmu

mmdetection

OpenMMLab Detection Toolbox and Benchmark

insightface

State-of-the-art 2D and 3D Face Analysis Project

transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

AdversarialNetsPapers

Awesome paper list with code about generative adversarial nets

pytorch-meta

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

dl-colab-notebooks

Try out deep learning models online on Google Colab

few-shot-object-detection

Implementations of few-shot object detection benchmarks

awesome-papers-fewshot

Collection for Few-shot Learning

awesome-zero-shot-learning

A curated list of papers, code and resources pertaining to zero shot learning

Few-Shot-Semantic-Segmentation-Papers

Few Shot Semantic Segmentation Papers

TTSR

[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution

RetinexNet

A Tensorflow implementation of RetinexNet

mimicry

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

DeepEMD

Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020

Fewshot_Detection

Few-shot Object Detection via Feature Reweighting

Few-Shot-Object-Detection-Dataset

Awesome-Super-Resolution

A curated list of awesome super-resolution resources.

iSeeBetter

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press

IGNN

[NeurIPS 2020] Cross-Scale Internal Graph Neural Network for Image Super-Resolution

IntraDA

Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision (CVPR 2020 Oral)

FSS-1000

FSS-1000, A 1000-class Dataset For Few-shot Segmentation

Context-Transformer

Context-Transformer: Tackling Object Confusion for Few-Shot Detection, AAAI 2020

GMAN

GANs with multiple Discriminators

DeepSEE

DeepSEE: A novel framework for Deep Disentangled Semantic Explorative Extreme Super-Resolution, ACCV 2020 (oral)

MSCNN

A Python 3 and Keras 2 implementation of MSCNN for people counting.

ExtremeLowLight

Code&Dataset : Learning an Adaptive Model for Extreme Low-light Raw Image Processing

3DLine-SLAM

3DLines-SLAM: A Monocular Vision Semi-Dense 3D Reconstruction Based on ORB-SLAM Abstract-Producing high-quality 3D maps and calculating more accurate camera pose has always been the goal of SLAM technology. The requirements of SLAM technology such as real-time, low computational cost, and low hardware cost are contradictory to the above objectives. For the issues listed above, we propose a novel semi-dense reconstruction algorithm based on the monocular ORB-SLAM system by matching the line segment features extracted from keyframes. Specifically, we build upon ORB-SLAM, the system first provides a set of keyframes and their corresponding camera poses and a series of map points in real-time. Then we use our developed a keyframe re-culling algorithm to culling redundant keyframes. Then an improved line segment extraction method is used to extract line segments in each keyframe. Finally, we use purely geometric constraints to generates accurate 3D scene model by matching 2D line segments from different keyframes. We thoroughly evaluate and in-depth analysis of our approach, the results show our system runs steadily and reliably. Not only the whole system has strong robustness, but also it can quickly generate an accurate 3d model online with low computational costs.

Low-Light-Image-Enhancement

We will provide the MATLAB implementation of the article "Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement" to facilitate future research in the field of low-light image enhancement.

Zero-Shot-Recognition-using-Dual-Visual-Semantic-Mapping-Paths

Zero Shot Recognition using Dual Visual Semantic Mapping Paths

LCCGAN-v2

Code for “Improving Generative Adversarial Networks with Local Coordinate Coding”