Stars
An implementation of Covariance Pooling, with the framwork of AlexNet and the dataset of UC Merced
This repository contains the code for Instance Credibility Inference (ICI).
Pytorch code of "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning", CVPR 2019.
Code for our ICCV 2019 paper PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
Spatial Transformer Networks. Refer to daviddao/spatial-transformer-tensorflow.
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
Datasets, Transforms and Models specific to Computer Vision
Official Pytorch implementation of CutMix regularizer
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).
Implementation of Non-local Block.
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
Code for the CVPR 2018 Oral Paper "Deep Layer Aggregation"
a pytorch code about Residual Attention Network. This code is based on two projects from
The implementation of VGG thesis is implemented under PyTorch framework
classical CNN impelementation with pytorch.Include VGGNet,BN-VGG,Inception-v1,BN-Inception,Resnet-v1,Resnet-v2,DenseNet and so on.
End-to-End Object Detection with Transformers
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
Non-official implement of Paper：CBAM: Convolutional Block Attention Module
Attention Branch Network (CIFAR100, ImageNet models)
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.