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Here is the research experience and code of the MG group
The core code of "Assisted learning for land use classification: The important role of semantic correlation between heterogeneous images"
Danfeng Hong, JIngliang Hu, Jing Yao, Jocelyn Chanussot, Xiao Xiang Zhu. Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model…
PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
VMamba: Visual State Space Models，code is based on mamba
[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
[CVPR 2023] Official implementation for "CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion."
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Train…
Codes for our CVPR 2021 paper "Deep Compositional Metric Learning"
(ICML 2020) This repo contains code for our paper "Revisiting Training Strategies and Generalization Performance in Deep Metric Learning" (https://arxiv.org/abs/2002.08473) to facilitate consistent…
PyTorch Implementation for Deep Metric Learning Pipelines
Pytorch implementation of the paper "Circle Loss: A Unified Perspective of Pair Similarity Optimization"
⛹️ Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
The semantic segmentation of remote sensing images
Remote Sensing Sar-Optical Land-use Classfication Pytorch Pytorch高分辨率遥感语义分割/地物分割/地物分类
Open source dataset; multimodal fusion；remote sensing；optical images; SAR images；deep learning