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A LaTeX Template for Dissertation Writing at the University of Electronic Science and Technology of China Since 2024
Apache RocketMQ is a cloud native messaging and streaming platform, making it simple to build event-driven applications.
🔥 官方推荐 🔥 大学春招、秋招、应届项目，SpringBoot3 + Java17 + SpringCloud Alibaba + Vue3 等技术架构，完成高仿铁路 12306 用户 + 抢票 + 订单 + 支付服务，帮助学生主打就业的项目。
Meta Learning for Semi-Supervised Few-Shot Classification
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Code for "Matching feature sets for few-shot image classification"-CVPR'2022.
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification (TIP2021)
code and trained models for "Attentional Feature Fusion"
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
This repository is the official implementation Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.
[PR22, Highly Cited Paper] Learning Attention-Guided Pyramidal Features for Few-shot Fine-grained Recognition
Code for 'Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning'
Few shot learning for human pose estimation
[ICML 2023] A Closer Look at Few-shot Classification Again
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
Awesome List of Attention Modules and Plug&Play Modules in Computer Vision
OpenMMLab FewShot Learning Toolbox and Benchmark
LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023.
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.
Self-attention、Non-local、SE、SK、CBAM、DANet
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
✔（已完结）最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】
Pytorch code of "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning", CVPR 2019.
