Pinned Repositories
Auto_painter
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose a model called auto-painter which can automatically generate compatible colors given a sketch. Wasserstein distance is used in training cGAN to overcome model collapse and enable the model converged much better. The new model is not only capable of painting hand-draw sketch with compatible colors, but also allowing users to indicate preferred colors. Experimental results on different sketch datasets show that the auto-painter performs better than other existing image-to-image methods.
Auto_painter_demo
The code of building a web demo for Auto_painter
CoupleGenerator
Generate your lover with your photo
CWD
Channel-wise Distillation for Semantic Segmentation
EMM-for-stock-prediction
We propose a model to analyze sentiment of online stock forum and use the information to predict stock volatility in the Chinese market. By generating a sentimental dictionary, we analyze the sentimental tendencies of each post as sentiment indicators. Such sentimental information will be fused with market data for prediction based on Recurrent Neural Networks (RNNs). We manually labeled the sentiment of forum post and make the data public available for research. Empirical evidence shows that 8 of the 10 stocks perform better with sentimental indicators.
ETC-Real-time-Per-frame-Semantic-video-segmentation
Enforcing temporal consistency in real-time per-frame semantic video segmentation
inceptionV2_finetune
Fine-tuning of inceptionV2 on CUB-200 Birds dataset in Tensorflow
SSIW
The code of 'The devil is in the labels: Semantic segmentation from sentences'.
structure_knowledge_distillation
The official code for the paper 'Structured Knowledge Distillation for Semantic Segmentation'. (CVPR 2019 ORAL) and extension to other tasks.
TorchDistiller
irfanICMLL's Repositories
irfanICMLL/structure_knowledge_distillation
The official code for the paper 'Structured Knowledge Distillation for Semantic Segmentation'. (CVPR 2019 ORAL) and extension to other tasks.
irfanICMLL/CoupleGenerator
Generate your lover with your photo
irfanICMLL/ETC-Real-time-Per-frame-Semantic-video-segmentation
Enforcing temporal consistency in real-time per-frame semantic video segmentation
irfanICMLL/TorchDistiller
irfanICMLL/Auto_painter
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose a model called auto-painter which can automatically generate compatible colors given a sketch. Wasserstein distance is used in training cGAN to overcome model collapse and enable the model converged much better. The new model is not only capable of painting hand-draw sketch with compatible colors, but also allowing users to indicate preferred colors. Experimental results on different sketch datasets show that the auto-painter performs better than other existing image-to-image methods.
irfanICMLL/Auto_painter_demo
The code of building a web demo for Auto_painter
irfanICMLL/SSIW
The code of 'The devil is in the labels: Semantic segmentation from sentences'.
irfanICMLL/CWD
Channel-wise Distillation for Semantic Segmentation
irfanICMLL/reid-strong-baseline
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
irfanICMLL/antialiased-cnns
Antialiasing cnns to improve stability and accuracy. In ICML 2019.
irfanICMLL/CC-FPSE
irfanICMLL/DeepLabV3Plus-Pytorch
DeepLabv3, DeepLabv3+ and pretrained weights on VOC & Cityscapes
irfanICMLL/detectron2
Detectron2 is FAIR's next-generation platform for object detection and segmentation.
irfanICMLL/guided-filter-pytorch
PyTorch implementation of Guided Image Filtering
irfanICMLL/SOLO
SOLO: Segmenting Objects by Locations https://arxiv.org/abs/1912.04488
irfanICMLL/AdelaiDet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
irfanICMLL/CaNet
The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"
irfanICMLL/CARAFE
Unofficial implementation of CARAFE: Content-Aware ReAssembly of FEatures. Pure pytorch imp
irfanICMLL/diffuser
Code for the paper "Planning with Diffusion for Flexible Behavior Synthesis"
irfanICMLL/EdgeNets
This repository contains the source code of our work on designing efficient CNNs for computer vision
irfanICMLL/ESPNetv2
A light-weight, power efficient, and general purpose convolutional neural network
irfanICMLL/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
irfanICMLL/FCHarDNet
Fully Convolutional HarDNet for Segmentation in Pytorch
irfanICMLL/Image_Harmonization_Datasets
Benchmark datasets and code used in our paper "DoveNet: Deep Image Harmonization via Domain Verification", CVPR2020. Useful for Image harmonization, image composition/compositing, etc.
irfanICMLL/mmdetection-distiller-CWD
This is a knowledge distillation toolbox based on mmdetection.
irfanICMLL/NRD_decoder
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
irfanICMLL/PointRend-simple-pytorch
a pytorch-based simple PointRend structure
irfanICMLL/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
irfanICMLL/Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
irfanICMLL/vision
Datasets, Transforms and Models specific to Computer Vision