WheatCao

WheatCao's Stars

• MedMNIST/MedMNIST

  [pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

  Language:Python1.1k169

• zhangjinCV/Noisy-COD

  🔥 [ECCV2024] Official Implementation of "Learning Camouflaged Object Detection from Noisy Pseudo Label"

  Language:Python151

• GuyHacohen/curriculum_learning

  Code implementing the experiments described in the paper "On The Power of Curriculum Learning in Training Deep Networks" by Hacohen & Weinshall (ICML 2019)

  Language:Python10725

• RingBDStack/VIB-GSL

  Code for "Graph Structure Learning with Variational Information Bottleneck" published in AAAI 2022

  Language:Python3310

• Easy-Shu/Beautiful-Visualization-with-Python

  python数据可视化之美

  Language:Jupyter Notebook353214

• BAAI-DCAI/Dataset-Pruning

  Dataset pruning for ImageNet and LAION-2B.

  Language:Python694

• Openning07/awesome-curriculum-learning

  some bravo or inspiring research works on the topic of curriculum learning

  22716

• ZZY816/COM

  Curricular Object Manipulation in LiDAR-based Object Detection（CVPR 2023）

  Language:Python371

• Emory-Melody/awesome-graph-reduction

  [IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.

  1338

• Hzzone/torch_clustering

  A pure PyTorch implementation of kmeans and GMM with distributed clustering.

  Language:Python436

• gr-b/W-Net-Pytorch

  Implementation of W-Net unsupervised semantic segmentation (Autoencoder with min-cut "graph connectedness" loss)

  Language:Python207

• zzhenggit/graph_cuts_loss

  code for ICCVW paper 'Graph Cuts Loss to Boost Model Accuracy and Generalizability for Medical Image Segmentation'.

  Language:Python164

• benedekrozemberczki/ClusterGCN

  A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

  Language:Python787136

• cszhangzhen/HGP-SL

  Hierarchical Graph Pooling with Structure Learning

  Language:Python33155

• Guang000/Awesome-Dataset-Distillation

  A curated list of awesome papers on dataset distillation and related applications.

  Language:HTML1.4k135

• jike5/P-MapNet

  Received by RAL

  Language:Python17511

• jwyang/graph-rcnn.pytorch

  [ECCV 2018] Official code for "Graph R-CNN for Scene Graph Generation"

  Language:Python732157

• microsoft/scene_graph_benchmark

  image scene graph generation benchmark

  Language:Python39086

• KaihuaTang/Scene-Graph-Benchmark.pytorch

  A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”

  Language:Jupyter Notebook1.1k228

• jjbrophy47/machine_unlearning

  Existing Literature about Machine Unlearning

  79097

• ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning

  A Survey on Interpretable Cross-modal Reasoning

  13

• Paranioar/Awesome_Matching_Pretraining_Transfering

  The Paper List of Large Multi-Modality Model, Parameter-Efficient Finetuning, Vision-Language Pretraining, Conventional Image-Text Matching for Preliminary Insight.

  40347

• c-yn/IRNeXt

  [ICML2023] IRNeXt: Rethinking Convolutional Network Design for Image Restoration

  Language:Python534

• femnn/RecurrentGPT-zh

  Language:Python2710

• FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling

  Experimental results obtained with the MinCutPool layer as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling"

  Language:Python26449

• AlphaJia/pytorch-faster-rcnn

  pytorch based implementation faster rcnn

  Language:Python44586

• CASIA-IVA-Lab/FastSAM

  Fast Segment Anything

  Language:Python7.5k711

• etzinis/unsup_speech_enh_adaptation

  Unsupervised domain adaptation for conversational speech enhancement using RemixIT

  Language:Jupyter Notebook525

• microsoft/DNS-Challenge

  This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.

  Language:Python1.1k415

• Le-Xiaohuai-speech/DPCRN_DNS3

  Implementation of paper "DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement"

  Language:Python18842