[TOC]
v1:Going deeper with convolutions v2:Batch Normalization: Accelerating Deep Network Training by ReducingInternal Covariate Shift v3:Rethinking the InceptionArchitecture for Computer Vision v4:Inception-v4,Inception-ResNet and the Impact of Residual Connections on Learning Aggregated ResidualTransformations for Deep Neural Networks Xception: DeepLearning with Depthwise Separable Convolutions
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks : https://arxiv.org/pdf/1611.05725.pdf
NASNet:Learning Transferable Architectures for Scalable Image Recognition:https://arxiv.org/pdf/1707.07012.pdf
ResNet v1: Deep Residual Learning for Image Recognition: https://arxiv.org/pdf/1512.03385.pdf
ResNet v2: Identity Mappings in Deep Residual Networks: https://arxiv.org/pdf/1603.05027.pdf
Wide Residual Networks: https://arxiv.org/pdf/1605.07146.pdf
DenseNet: Densely Connected Convolutional Networks: https://arxiv.org/pdf/1608.06993.pdf
ResNeXt: Aggregated Residual Transformations for Deep Neural Networks: https://arxiv.org/pdf/1611.05431.pdf
Residual Attention Network for Image Classification: https://arxiv.org/pdf/1704.06904.pdf
SENet: Squeeze-and-Excitation Networks: https://arxiv.org/pdf/1709.01507.pdf
Memory-Efficient Implementation of DenseNets: https://arxiv.org/pdf/1707.06990.pdf
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications: https://arxiv.org/pdf/1704.04861.pdf ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices:https://arxiv.org/pdf/1707.01083.pdf MobileNetV2: Inverted Residuals and Linear Bottlenecks :https://arxiv.org/pdf/1801.04381.pdf
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design:https://arxiv.org/pdf/1807.11164.pdf
R-CNN: https://arxiv.org/pdf/1311.2524.pdf
SPPNet Spatial Pyramid Pooling in Deep Convolutional Networks : https://arxiv.org/pdf/1406.4729&ie=utf-8&sc_us=14732595831688094247.pdf
Fast R-CNN: https://arxiv.org/pdf/1504.08083.pdf
Faster R-CNN: https://arxiv.org/pdf/1506.01497.pdf
FPN: Feature Pyramid Networks for Object Detection: https://arxiv.org/pdf/1612.03144.pdf
Mask R-CNN: https://arxiv.org/pdf/1703.06870.pdf
Cascade R-CNN: Delving into High Quality Object Detection:https://arxiv.org/pdf/1712.00726.pdf
SSD Single Shot MultiBox Detector：https://arxiv.org/pdf/1512.02325.pdf
DSSD : Deconvolutional Single Shot Detector：https://arxiv.org/pdf/1701.06659.pdf
You Only Look Once: Unified, Real-Time Object Detection：https://arxiv.org/abs/1506.02640
YOLO9000 Better, Faster, Stronger: https://arxiv.org/pdf/1612.08242.pdf
Retinanet：https://arxiv.org/pdf/1708.02002.pdf
G-CNN: an Iterative Grid Based Object Detector ：https://arxiv.org/pdf/1512.07729.pdf
R-FCN: Object Detection via Region-based Fully Convolutional Networks: https://arxiv.org/pdf/1605.06409.pdf
R-FCN-3000 ：https://arxiv.org/pdf/1712.01802.pdf
DetNet: A Backbone network for Object : https://arxiv.org/abs/1804.06215
Relation Networks for Object Detection : https://arxiv.org/abs/1711.11575
RefineDet: Single-Shot Refinement Neural Network for Object Detection: https://arxiv.org/abs/1711.06897
Bag of Freebies for Training Object Detection Neural Networks:https://arxiv.org/abs/1902.04103v2
FCOS: Fully Convolutional One-Stage Object Detection: https://arxiv.org/pdf/1904.01355.pdf
Fully Convolutional Networks for Semantic Segmentation：https://arxiv.org/pdf/1411.4038.pdf
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs：http://arxiv.org/pdf/1412.7062.pdf
U-Net: Convolutional Networks for Biomedical Image Segmentation：https://arxiv.org/pdf/1505.04597.pdf
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs: http://arxiv.org/pdf/1606.00915
DeepLab v2: Rethinking Atrous Convolution for Semantic Image Segmentation: https://arxiv.org/pdf/1706.05587.pdf
DeepLab v3: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation: https://arxiv.org/abs/1802.02611
UNet++: A Nested U-Net Architecture for Medical Image Segmentation : https://arxiv.org/pdf/1807.10165.pdf
FCIS:Fully Convolutional Instance-aware Semantic Segmentation: https://arxiv.org/pdf/1611.07709.pdf
PAN: Path Aggregation Network for Instance Segmentation: https://arxiv.org/pdf/1803.01534.pdf
Mask Scoring R-CNN: https://arxiv.org/pdf/1903.00241.pdf
YOLACT:Real-time Instance Segmentation: https://arxiv.org/pdf/1904.02689.pdf
DeepPose: Human Pose Estimation via Deep Neural Networks: https://arxiv.org/pdf/1312.4659.pdf
Stacked Hourglass Networks for Human Pose Estimation: https://arxiv.org/pdf/1603.06937.pdf
DensePose: Dense Human Pose Estimation In The Wild：https://arxiv.org/pdf/1802.00434.pdf
On-line Boosting and Vision：http://read.pudn.com/downloads108/doc/445004/01640768.pdf
Online Object Tracking: A Benchmark：http://faculty.ucmerced.edu/mhyang/papers/cvpr13_benchmark.pdf
Transferring Rich Feature Hierarchies for Robust Visual Tracking：https://arxiv.org/pdf/1501.04587.pdf
Visual tracking with fully convolutional networks(FCNT): http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wang_Visual_Tracking_With_ICCV_2015_paper.pdf
Learning to Track at 100 FPS with Deep Regression Networks(GOTURN):https://arxiv.org/pdf/1604.01802.pdf
Fully-Convolutional Siamese Networks for Object Tracking : https://arxiv.org/pdf/1606.09549.pdf
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking:https://arxiv.org/pdf/1608.03773.pdf
MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks: https://kpzhang93.github.io/MTCNN_face_detection_alignment/
Bayesian Face Revisited: A Joint Formulation：https://www.microsoft.com/en-us/research/wp-content/uploads/2012/01/JointBayesian.pdf
DeepID1: Deep Learning Face Representation from Predicting 10,000 Classes https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf
DeepID2: Deep Learning Face Representation by Joint Identification-Verification https://arxiv.org/pdf/1406.4773.pdf
DeepID2+: Deeply learned face representations are sparse, selective, and robust： https://arxiv.org/abs/1412.1265v1
DeepID3: Face Recognition with Very Deep Neural Networks https://arxiv.org/pdf/1502.00873.pdf
FaceNet: A Unified Embedding for Face Recognition and Clustering: https://arxiv.org/pdf/1503.03832.pdf
Deep Face Recognition: http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
A Discriminative Feature Learning Approach for Deep Face Recognition : http://ydwen.github.io/papers/WenECCV16.pdf
SphereFace: Deep Hypersphere Embedding for Face Recognition: https://arxiv.org/pdf/1704.08063.pdf
ArcFace/InsightFace : Additive Angular Margin Loss for Deep Face Recognition : https://arxiv.org/abs/1801.07698
MobileFaceNets: Efficient CNNs for Accurate RealTime Face Verification on Mobile Devices: https://arxiv.org/ftp/arxiv/papers/1804/1804.07573.pdf
DocFace+: ID Document to Selfie* Matching: https://arxiv.org/pdf/1809.05620.pdf
Low-Resolution Face Recognition: https://arxiv.org/pdf/1811.08965.pdf
Accurate and Efficient Similarity Search for Large Scale Face Recognition:https://arxiv.org/pdf/1806.00365.pdf
CRNN: An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition: https://arxiv.org/pdf/1507.05717.pdf
Synthetic Data for Text Localisation in Natural Images: https://arxiv.org/pdf/1604.06646.pdf
Scene text detection via holistic, multi-channel prediction: https://arxiv.org/pdf/1606.09002.pdf
CPTN: Detecting Text in Natural Image with Connectionist Text Proposal Network: https://arxiv.org/abs/1609.03605
SegLink: Detecting Oriented Text in Natural Images by Linking Segments: https://arxiv.org/pdf/1703.06520.pdf
Attention-based Extraction of Structured Information from Street View Imagery: https://arxiv.org/abs/1704.03549
EAST: An Efficient and Accurate Scene Text Detector: https://arxiv.org/pdf/1704.03155.pdf
TextBoxes: A Fast Text Detector with a Single Deep Neural Network: https://arxiv.org/pdf/1611.06779.pdf
TextBoxes++: A Single-Shot Oriented Scene Text Detector: https://arxiv.org/pdf/1801.02765.pdf
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network:https://arxiv.org/pdf/1711.08324v1.pdf
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification:
Co-Learning Feature Fusion Maps from PET-CTImages of Lung Cancer: https://arxiv.org/pdf/1810.02492.pdf
Lung Nodule Classification using Deep Local-Global Networks:https://arxiv.org/ftp/arxiv/papers/1904/1904.10126.pdf
Gated-Dilated Networks for Lung Nodule Classification in CT scans:https://arxiv.org/ftp/arxiv/papers/1901/1901.00120.pdf
DIAGNOSTIC CLASSIFICATION OF LUNG NODULES USING 3D NEURAL NETWORKS: https://arxiv.org/pdf/1803.07192.pdf
Context-Aware Crowd Counting：https://arxiv.org/pdf/1811.10452.pdf
总结：https://github.com/gjy3035/Awesome-Crowd-Counting
参考：异常检测概述
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network : https://arxiv.org/pdf/1806.00746
Future Frame Prediction for Anomaly Detection – A New Baseline: https://arxiv.org/pdf/1712.09867.pdf
Real-world Anomaly Detection in Surveillance Videos: https://arxiv.org/pdf/1801.04264.pdf
PReLU:Delving Deep into Rectifiers:Surpassing Human-Level Performance on ImageNet Classification:https://arxiv.org/pdf/1502.01852.pdf
ELU: FAST AND ACCURATE DEEP NETWORK LEARNING BY EXPONENTIAL LINEAR UNITS : https://arxiv.org/pdf/1511.07289.pdf
SELU: Self-Normalizing Neural Networks: https://arxiv.org/pdf/1706.02515.pdf
Higher Order Recurrent Neural Networks: https://arxiv.org/pdf/1605.00064.pdf