superglue learning matching networks
BIgdataAIML-IBM-A neural networks deep dive - An introduction to neural networks and their programming
https://developer.ibm.com/articles/cc-cognitive-neural-networks-deep-dive/ By M. Tim Jones, Published July 23, 2017 Neural networks have been around f ......
BigdataAIML-ML-Models for machine learning Explore the ideas behind machine learning models and some key algorithms used for each
最好的机器学习教程系列：https://developer.ibm.com/articles/cc-models-machine-learning/ By M. Tim Jones, Published December 4, 2017 Models for machine learning Alg ......
Relation Networks for Object Detection
Relation Networks for Object Detection * Authors: [[Han Hu]], [[Jiayuan Gu]], [[Zheng Zhang]], [[Jifeng Dai]], [[Yichen Wei]] DOI: 10.1109/CVPR.2018.0 ......
Local Relation Networks for Image Recognition： LRNet
Local Relation Networks for Image Recognition * Authors: [[Han Hu]], [[Zheng Zhang]], [[Zhenda Xie]], [[Stephen Lin]] DOI: 10.1109/ICCV.2019.00356 @in ......
Dual Attention Network for Scene Segmentation：双线并行的注意力
Dual Attention Network for Scene Segmentation * Authors: [[Jun Fu]], [[Jing Liu]], [[Haijie Tian]], [[Yong Li]], [[Yongjun Bao]], [[Zhiwei Fang]], [[H ......
Deep Residual Learning for Image Recognition：ResNet
Deep Residual Learning for Image Recognition * Authors: [[Kaiming He]], [[Xiangyu Zhang]], [[Shaoqing Ren]], [[Jian Sun]] DOI: 10.1109/CVPR.2016.90 初读 ......
Squeeze-and-Excitation Networks：SENet，早期cv中粗糙的注意力
Squeeze-and-Excitation Networks * Authors: [[Jie Hu]], [[Li Shen]], [[Samuel Albanie]], [[Gang Sun]], [[Enhua Wu]] Local library 初读印象 comment:: (SENet ......
Fully convolutional networks for semantic segmentation
Fully convolutional networks for semantic segmentation * Authors: [[Jonathan Long]], [[Evan Shelhamer]], [[Trevor Darrell]] DOI: 10.1109/CVPR.2015.729 ......
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation * Authors: [[Olaf Ronneberger]], [[Philipp Fischer]], [[Thomas Brox]] Local library 初读 ......
Non-local Neural Networks 第一次将自注意力用于cv
Non-local Neural Networks * Authors: [[Xiaolong Wang]], [[Ross Girshick]], [[Abhinav Gupta]], [[Kaiming He]] Local library 初读印象 comment:: (NonLocal)过去 ......
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation * Authors: [[Guosheng Lin]], [[Anton Milan]], [[Chunhua Shen]], [[ ......
Expectation-Maximization Attention Networks for Semantic Segmentation 使用了EM算法的注意力
Expectation-Maximization Attention Networks for Semantic Segmentation * Authors: [[Xia Li]], [[Zhisheng Zhong]], [[Jianlong Wu]], [[Yibo Yang]], [[Zho ......
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network * Authors: [[Wenzhe Shi]], [[Jose Caballer ......
Pyramid Scene Parsing Network
Pyramid Scene Parsing Network * Authors: [[Hengshuang Zhao]], [[Jianping Shi]], [[Xiaojuan Qi]], [[Xiaogang Wang]], [[Jiaya Jia]] DOI: 10.1109/CVPR.20 ......
Asymmetric Non-Local Neural Networks for Semantic Segmentation 非对称注意力
Asymmetric Non-Local Neural Networks for Semantic Segmentation * Authors: [[Zhen Zhu]], [[Mengdu Xu]], [[Song Bai]], [[Tengteng Huang]], [[Xiang Bai]] ......
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers * Authors: [[Jiacong Xu]], [[Zixiang Xiong]], [[Shankar P. Bhattacharyya ......
PSANet: Point-wise Spatial Attention Network for Scene Parsing双向注意力
PSANet: Point-wise Spatial Attention Network for Scene Parsing * Authors: [[Hengshuang Zhao]], [[Yi Zhang]], [[Shu Liu]], [[Jianping Shi]], [[Chen Cha ......
Object Tracking Network Based on Deformable Attention Mechanism
Object Tracking Network Based on Deformable Attention Mechanism Local library 初读印象 comment:: （DeTrack）采用基于可变形注意力机制的编码器模块和基于自注意力机制的编码器模块相结合的方式进行特征交互。基于 ......
Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images
Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images * Authors: [[Bowei Du]], [[Yecheng ......
A Deformable Attention Network for High-Resolution Remote Sensing Images Semantic Segmentation可变形注意力
A Deformable Attention Network for High-Resolution Remote Sensing Images Semantic Segmentation * Authors: [[Renxiang Zuo]], [[Guangyun Zhang]], [[Rong ......
Learning to Rank — xgboost 2.0.2
* [Learning to Rank — xgboost 2.0.2 documentation](https://xgboost.readthedocs.io/en/stable/tutorials/learning_to_rank.html)* [XGBoost的原理、公式推导、Python实 ......
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance 基于图像和视频的小对象指南使用深度学习进行检测：的案例研究海上监视 1 ......
Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives
应对行人信息丢失、噪声干扰、视角错位等挑战 局部特征学习：人体分割、姿态估计、语义分割、属性注释、混合法 ***语义分割*** ECCV-2020）用于行人重识别的身份引导人类语义解析 https://blog.51cto.com/u_14300986/5466923 ......
神经网络优化篇：机器学习基础（Basic Recipe for Machine Learning）
机器学习基础 下图就是在训练神经网络用到的基本方法：（尝试这些方法，可能有用，可能没用） 这是在训练神经网络时用到地基本方法，初始模型训练完成后，首先要知道算法的偏差高不高，如果偏差较高，试着评估训练集或训练数据的性能。如果偏差的确很高，甚至无法拟合训练集，那么要做的就是选择一个新的网络，比如含有更 ......
【scikit-learn基础】--『预处理』之 正则化
数据的预处理是数据分析，或者机器学习训练前的重要步骤。通过数据预处理，可以 提高数据质量，处理数据的缺失值、异常值和重复值等问题，增加数据的准确性和可靠性 整合不同数据，数据的来源和结构可能多种多样，分析和训练前要整合成一个数据集 提高数据性能，对数据的值进行变换，规约等（比如无量纲化），让算法更加 ......
Machine Learning in Python
Metric Formula Interpretation Accuracy $ \frac{TP+TN}{TP+TN+FP+FN} $ Overall performance of model Precision $ \frac{TP}{TP+FN} $ How accurate the posi ......
Ansor：Generating High-Performance Tensor Program for Deep Learning
Ansor：Generating High-Performance Tensor Program for Deep Learning Abstract 高性能的张量程序对于保证深度神经网络的高效执行十分关键，但是在不同硬件平台上获取高性能的张量程序并不容易。近年的研究中，深度学习系统依赖硬件供应商提 ......
SiReN Sign-Aware Recommendation Using Graph Neural Networks论文阅读笔记
Abstract 目前使用GNN的推荐系统主要利用高评分的正向用户-物品交互信息。但是如何利用低评分来表示用户的偏好是一个挑战，因为低评分仍然可以提供有用的信息。所以在本文中提出了基于GNN模型的有符号感知推荐系统SiReN，SiReN有三个关键组件 构造一个符号二部图更精确的表示用户的偏好，分为两 ......
RLHF · PbRL | 选择 near on-policy query，加速 policy learning 收敛速度
Query-Policy Misalignment：选择的看似 informative 的 query，实际上可能与 RL agent 的兴趣不一致，因此对 policy learning 几乎没有帮助，最终导致 feedback-efficiency 低下。 ......
Fully Attentional Network for Semantic Segmentation：FLANet
Fully Attentional Network for Semantic Segmentation * Authors: [[Qi Song]], [[Jie Li]], [[Chenghong Li]], [[Hao Guo]], [[Rui Huang]] 初读印象 comment:: (F ......