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	🔗	🔗
2	ImageNet Classification with Deep Convolutional Neural Networks	AlexNet	🔗	🔗
3	VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION	VGGNet	🔗	🔗
4	Visualizing and Understanding Convolutional Networks	ZFNet，卷积网络可视化，反卷积网络	🔗	🔗
5	Going deeper with convolutions	GoogLeNet，Inception-v1	🔗	🔗
6	Rich feature hierarchies for accurate object detection and semantic segmentation	R-CNN	🔗	🔗
7	Generative Adversarial Nets	GAN	🔗	🔗
8	Selective Search for Object Recognition	Selective Search算法	🔗	🔗
9	Efficient Graph-Based Image Segmentation	NULL	🔗	🔗
10	Batch Normalization：Accelerating Deep Network Training by Reducing Internal Covariate Shift	Batch Normalization，BN-Inception	🔗	🔗
11	Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images	GraphCut算法	🔗	🔗
12	“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts	GrabCut算法	🔗	🔗
13	Topological Structural Analysis of Digitized Binary Images by Border Following	Border Following，cv::findContours原理	🔗	🔗
14	Rethinking the Inception Architecture for Computer Vision	Inception-v2，Inception-v3	🔗	🔗
15	U-Net: Convolutional Networks for Biomedical Image Segmentation	U-Net	🔗	🔗
16	Fully Convolutional Networks for Semantic Segmentation	FCN，shift-and-stitch，backwards convolution，deconvolution	🔗	🔗
17	Deep Residual Learning for Image Recognition	ResNet	🔗	🔗
18	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	Inception-v4，Inception-ResNet	🔗	🔗
19	Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition	SPP-net	🔗	🔗
20	Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis	Elastic Distortions	🔗	🔗
21	Fast R-CNN	Fast R-CNN	🔗	🔗
22	Layer Normalization	Layer Normalization	🔗	🔗
23	Attention Is All You Need	Transformer，Multi-Head Attention	🔗	🔗
24	Faster R-CNN：Towards Real-Time Object Detection with Region Proposal Networks	Faster R-CNN，Region Proposal Networks（RPN）	🔗	🔗
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	🔗	🔗
26	GAUSSIAN ERROR LINEAR UNITS (GELUS)	Gaussian Error Linear Unit（GELU）激活函数，Sigmoid Linear Unit（SiLU）激活函数	🔗	🔗
27	You Only Look Once： Unified, Real-Time Object Detection	YOLOv1	🔗	🔗
28	YOLO9000：Better, Faster, Stronger	YOLOv2，YOLO9000	🔗	🔗
29	YOLOv3：An Incremental Improvement	YOLOv3，Darknet-53	🔗	🔗
30	AN IMAGE IS WORTH 16X16 WORDS：TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE	Vision Transformer（ViT）	🔗	🔗
31	Distribution-Aware Coordinate Representation for Human Pose Estimation	DARK	🔗	🔗
32	ViTPose：Simple Vision Transformer Baselines for Human Pose Estimation	ViTPose，Human Pose Estimation	🔗	🔗
33	Swin Transformer：Hierarchical Vision Transformer using Shifted Windows	Swin Transformer	🔗	🔗
34	Deep High-Resolution Representation Learning for Visual Recognition	HRNet	🔗	🔗
35	FlowNet：Learning Optical Flow with Convolutional Networks	FlowNet	🔗	🔗
36	FlowNet 2.0：Evolution of Optical Flow Estimation with Deep Networks	FlowNet2	🔗	🔗
37	3D Convolutional Neural Networks for Human Action Recognition	3D卷积	🔗	🔗
38	3D U-Net：Learning Dense Volumetric Segmentation from Sparse Annotation	3D U-Net	🔗	🔗
39	V-Net：Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation	V-Net，dice loss	🔗	🔗
40	SURF：Speeded Up Robust Features	SURF，U-SURF	🔗	🔗
41	nnU-Net：Self-adapting Framework for U-Net-Based Medical Image Segmentation	nnU-Net	🔗	🔗
42	Histograms of Oriented Gradients for Human Detection	HOG	🔗	🔗
43	PERCEIVER IO：A GENERAL ARCHITECTURE FOR STRUCTURED INPUTS & OUTPUTS	Perceiver IO	🔗	🔗
44	Densely Connected Convolutional Networks	DenseNet	🔗	🔗
45	SimCC：a Simple Coordinate Classification Perspective for Human Pose Estimation	SimCC	🔗	🔗
46	Network In Network	NIN	🔗	🔗
47	Aggregated Residual Transformations for Deep Neural Networks	ResNeXt	🔗	🔗
48	CSPNET：A NEW BACKBONE THAT CAN ENHANCE LEARNING CAPABILITY OF CNN	CSPNet	🔗	🔗
49	Feature Pyramid Networks for Object Detection	FPN	🔗	🔗
50	Mask R-CNN	Mask R-CNN	🔗	🔗
51	Path Aggregation Network for Instance Segmentation	PANet	🔗	🔗
52	YOLOv4：Optimal Speed and Accuracy of Object Detection	YOLOv4	🔗	🔗
53	YOLOv5	YOLOv5	\	🔗
54	YOLOX：Exceeding YOLO Series in 2021	YOLOX	🔗	🔗
55	Focal Loss for Dense Object Detection	Focal Loss，RetinaNet	🔗	🔗
56	RTMDet：An Empirical Study of Designing Real-Time Object Detectors	RTMDet	🔗	🔗
57	RTMPose：Real-Time Multi-Person Pose Estimation based on MMPose	RTMPose	🔗	🔗
58	Effective Whole-body Pose Estimation with Two-stages Distillation	DWPose	🔗	🔗
59	OpenPose：Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields	OpenPose	🔗	🔗
60	GPT系列论文	GPT1，GPT2，GPT3，GPT3.5，InstructGPT，GPT4	🔗	🔗
61	Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation	SCAI	🔗	🔗
62	Simple Baselines for Human Pose Estimation and Tracking	SimpleBaseline	🔗	🔗
63	TaG-Net：Topology-Aware Graph Network for Centerline-Based Vessel Labeling	TaG-Net，vessel labeling，vessel segmentation	🔗	🔗
64	OverFeat：Integrated Recognition, Localization and Detection using Convolutional Networks	OverFeat，sliding window	🔗	🔗
65	Bag of Tricks for Image Classification with Convolutional Neural Networks	ResNet-vc，ResNet-vd	🔗	🔗
66	R-FCN：Object Detection via Region-based Fully Convolutional Networks	R-FCN	🔗	🔗
67	Deformable Convolutional Networks	DCN	🔗	🔗
68	A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses	鱼眼相机校正	🔗	🔗
69	PP-YOLO：An Effective and Efficient Implementation of Object Detector	PP-YOLO	🔗	🔗
70	UnitBox：An Advanced Object Detection Network	UnitBox，IoU loss	🔗	🔗
71	IoU-aware Single-stage Object Detector for Accurate Localization	IoU-aware loss	🔗	🔗
72	PP-YOLOv2：A Practical Object Detector	PP-YOLOv2	🔗	🔗
73	BERT：Pre-training of Deep Bidirectional Transformers for Language Understanding	BERT	🔗	🔗
74	Group Normalization	Batch Norm，Layer Norm，Instance Norm，Group Norm	🔗	🔗
75	FCOS：Fully Convolutional One-Stage Object Detection	FCOS	🔗	🔗
76	Machine Learning for High-Speed Corner Detection	FAST	🔗	🔗
77	TOOD：Task-aligned One-stage Object Detection	TOOD	🔗	🔗
78	Generalized Focal Loss：Learning Qualified and Distributed Bounding Boxes for Dense Object Detection	GFL，QFL，DFL	🔗	🔗
79	BRISK：Binary Robust invariant scalable keypoints	BRISK	🔗	🔗
80	Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection	ATSS	🔗	🔗
81	VarifocalNet：An IoU-aware Dense Object Detector	VFNet，Varifocal Loss，IACS	🔗	🔗
82	PP-YOLOE：An evolved version of YOLO	PP-YOLOE	🔗	🔗
83	EfficientNet：Rethinking Model Scaling for Convolutional Neural Networks	EfficientNet	🔗	🔗
84	MobileNets：Efficient Convolutional Neural Networks for Mobile Vision Applications	MobileNet	🔗	🔗
85	MobileNetV2：Inverted Residuals and Linear Bottlenecks	MobileNetV2	🔗	🔗
86	An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection	VoVNet	🔗	🔗
87	Enriching Variety of Layer-wise Learning Information by Gradient Combination	PRN	🔗	🔗
88	SSD：Single Shot MultiBox Detector	SSD	🔗	🔗
89	Scaled-YOLOv4：Scaling Cross Stage Partial Network	Scaled-YOLOv4，YOLOv4-CSP，YOLOv4-Tiny，YOLOv4-Large	🔗	🔗
90	You Only Learn One Representation：Unified Network for Multiple Tasks	YOLOR	🔗	🔗
91	Designing Network Design Strategies Through Gradient Path Analysis	ELAN	🔗	🔗
92	ShuffleNet：An Extremely Efficient Convolutional Neural Network for Mobile Devices	ShuffleNet	🔗	🔗
93	Squeeze-and-Excitation Networks	SENet	🔗	🔗
94	ShuffleNet V2：Practical Guidelines for Efficient CNN Architecture Design	ShuffleNet V2	🔗	🔗
95	RepVGG：Making VGG-style ConvNets Great Again	RepVGG	🔗	🔗
96	YOLOv7：Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors	YOLOv7	🔗	🔗
97	Generalized Intersection over Union：A Metric and A Loss for Bounding Box Regression	GIoU	🔗	🔗
98	Distance-IoU Loss：Faster and Better Learning for Bounding Box Regression	DIoU，CIoU	🔗	🔗
99	SIoU Loss：More Powerful Learning for Bounding Box Regression	SIoU	🔗	🔗
100	RE-PARAMETERIZING YOUR OPTIMIZERS RATHER THAN ARCHITECTURES	RepOptimizers，RepOpt-VGG	🔗	🔗