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Papers on Artificial Intelligence🎯AI领域经典论文+博客讲解🚀
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AI_Papers

序号 论文题目 关键字 原文 博客讲解
1 Gradient-Based Learning Applied to Document Recognition LeNet5 链接 链接
2 ImageNet Classification with Deep Convolutional Neural Networks AlexNet 链接 链接
3 VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION VGGNet 链接 链接
4 Visualizing and Understanding Convolutional Networks ZFNet，卷积网络可视化，反卷积网络 链接 链接
5 Going deeper with convolutions GoogLeNet，Inception-v1 链接 链接
6 Rich feature hierarchies for accurate object detection and semantic segmentation R-CNN 链接 链接
7 Generative Adversarial Nets GAN 链接 链接
8 Selective Search for Object Recognition Selective Search算法 链接 链接
9 Efficient Graph-Based Image Segmentation NULL 链接 链接
10 Batch Normalization：Accelerating Deep Network Training by Reducing Internal Covariate Shift Batch Normalization，BN-Inception 链接 链接
11 Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images GraphCut算法 链接 链接
12 “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts GrabCut算法 链接 链接
13 Topological Structural Analysis of Digitized Binary Images by Border Following Border Following，cv::findContours原理 链接 链接
14 Rethinking the Inception Architecture for Computer Vision Inception-v2，Inception-v3 链接 链接
15 U-Net: Convolutional Networks for Biomedical Image Segmentation U-Net 链接 链接
16 Fully Convolutional Networks for Semantic Segmentation FCN，shift-and-stitch，backwards convolution，deconvolution 链接 链接
17 Deep Residual Learning for Image Recognition ResNet 链接 链接
18 Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Inception-v4，Inception-ResNet 链接 链接
19 Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition SPP-net 链接 链接
20 Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis Elastic Distortions 链接 链接
21 Fast R-CNN Fast R-CNN 链接 链接
22 Layer Normalization Layer Normalization 链接 链接
23 Attention Is All You Need Transformer，Multi-Head Attention 链接 链接
24 Faster R-CNN：Towards Real-Time Object Detection with Region Proposal Networks Faster R-CNN，Region Proposal Networks（RPN） 链接 链接
25 FAST AND ACCURATE DEEP NETWORK LEARNING BY EXPONENTIAL LINEAR UNITS (ELUS) exponential linear unit（ELU）激活函数，Shifted ReLU（SReLU）激活函数 链接 链接
26 GAUSSIAN ERROR LINEAR UNITS (GELUS) Gaussian Error Linear Unit（GELU）激活函数，Sigmoid Linear Unit（SiLU）激活函数 链接 链接
27 You Only Look Once： Unified, Real-Time Object Detection YOLOv1 链接 链接
28 YOLO9000：Better, Faster, Stronger YOLOv2，YOLO9000 链接 链接
29 YOLOv3：An Incremental Improvement YOLOv3，Darknet-53 链接 链接
30 AN IMAGE IS WORTH 16X16 WORDS：TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE Vision Transformer（ViT） 链接 链接
31 Distribution-Aware Coordinate Representation for Human Pose Estimation DARK 链接 链接
32 ViTPose：Simple Vision Transformer Baselines for Human Pose Estimation ViTPose，Human Pose Estimation 链接 链接
33 Swin Transformer：Hierarchical Vision Transformer using Shifted Windows Swin Transformer 链接 链接
34 Deep High-Resolution Representation Learning for Visual Recognition HRNet 链接 链接
35 FlowNet：Learning Optical Flow with Convolutional Networks FlowNet 链接 链接
36 FlowNet 2.0：Evolution of Optical Flow Estimation with Deep Networks FlowNet2 链接 链接
37 3D Convolutional Neural Networks for Human Action Recognition 3D卷积 链接 链接
38 3D U-Net：Learning Dense Volumetric Segmentation from Sparse Annotation 3D U-Net 链接 链接
39 V-Net：Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation V-Net，dice loss 链接 链接
40 SURF：Speeded Up Robust Features SURF，U-SURF 链接 链接
41 nnU-Net：Self-adapting Framework for U-Net-Based Medical Image Segmentation nnU-Net 链接 链接
42 Histograms of Oriented Gradients for Human Detection HOG 链接 链接
43 PERCEIVER IO：A GENERAL ARCHITECTURE FOR STRUCTURED INPUTS & OUTPUTS Perceiver IO 链接 链接
44 Densely Connected Convolutional Networks DenseNet 链接 链接
45 SimCC：a Simple Coordinate Classification Perspective for Human Pose Estimation SimCC 链接 链接
46 Network In Network NIN 链接 链接
47 Aggregated Residual Transformations for Deep Neural Networks ResNeXt 链接 链接
48 CSPNET：A NEW BACKBONE THAT CAN ENHANCE LEARNING CAPABILITY OF CNN CSPNet 链接 链接
49 Feature Pyramid Networks for Object Detection FPN 链接 链接
50 Mask R-CNN Mask R-CNN 链接 链接
51 Path Aggregation Network for Instance Segmentation PANet 链接 链接
52 YOLOv4：Optimal Speed and Accuracy of Object Detection YOLOv4 链接 链接
53 YOLOv5 YOLOv5 \ 链接
54 YOLOX：Exceeding YOLO Series in 2021 YOLOX 链接 链接
55 Focal Loss for Dense Object Detection Focal Loss，RetinaNet 链接 链接
56 RTMDet：An Empirical Study of Designing Real-Time Object Detectors RTMDet 链接 链接
57 RTMPose：Real-Time Multi-Person Pose Estimation based on MMPose RTMPose 链接 链接
58 Effective Whole-body Pose Estimation with Two-stages Distillation DWPose 链接 链接
59 OpenPose：Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields OpenPose 链接 链接
60 GPT系列论文 GPT1，GPT2，GPT3，GPT3.5，InstructGPT，GPT4 链接 链接
61 Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation SCAI 链接 链接
62 Simple Baselines for Human Pose Estimation and Tracking SimpleBaseline 链接 链接
63 TaG-Net：Topology-Aware Graph Network for Centerline-Based Vessel Labeling TaG-Net，vessel labeling，vessel segmentation 链接 链接
64 OverFeat：Integrated Recognition, Localization and Detection using Convolutional Networks OverFeat，sliding window 链接 链接
65 Bag of Tricks for Image Classification with Convolutional Neural Networks ResNet-vc，ResNet-vd 链接 链接
66 R-FCN：Object Detection via Region-based Fully Convolutional Networks R-FCN 链接 链接
67 Deformable Convolutional Networks DCN 链接 链接
68 A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses 鱼眼相机校正 链接 链接
69 PP-YOLO：An Effective and Efficient Implementation of Object Detector PP-YOLO 链接 链接
70 UnitBox：An Advanced Object Detection Network UnitBox，IoU loss 链接 链接
71 IoU-aware Single-stage Object Detector for Accurate Localization IoU-aware loss 链接 链接
72 PP-YOLOv2：A Practical Object Detector PP-YOLOv2 链接 链接
73 BERT：Pre-training of Deep Bidirectional Transformers for Language Understanding BERT 链接 链接
74 Group Normalization Batch Norm，Layer Norm，Instance Norm，Group Norm 链接 链接
75 FCOS：Fully Convolutional One-Stage Object Detection FCOS 链接 链接
76 Machine Learning for High-Speed Corner Detection FAST 链接 链接
77 TOOD：Task-aligned One-stage Object Detection TOOD 链接 链接
78 Generalized Focal Loss：Learning Qualified and Distributed Bounding Boxes for Dense Object Detection GFL，QFL，DFL 链接 链接
79 BRISK：Binary Robust invariant scalable keypoints BRISK 链接 链接
80 Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection ATSS 链接 链接
81 VarifocalNet：An IoU-aware Dense Object Detector VFNet，Varifocal Loss，IACS 链接 链接