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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 :

NASNet:Learning Transferable Architectures for Scalable Image Recognition:

ResNet v1: Deep Residual Learning for Image Recognition:

ResNet v2: Identity Mappings in Deep Residual Networks:

Wide Residual Networks:

DenseNet: Densely Connected Convolutional Networks:

ResNeXt: Aggregated Residual Transformations for Deep Neural Networks:

Residual Attention Network for Image Classification:

SENet: Squeeze-and-Excitation Networks:

Memory-Efficient Implementation of DenseNets:


MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices: MobileNetV2: Inverted Residuals and Linear Bottlenecks :

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design:



SPPNet Spatial Pyramid Pooling in Deep Convolutional Networks :

Fast R-CNN:

Faster R-CNN:

FPN: Feature Pyramid Networks for Object Detection:

Mask R-CNN:

Cascade R-CNN: Delving into High Quality Object Detection:

SSD Single Shot MultiBox Detector:

DSSD : Deconvolutional Single Shot Detector:

You Only Look Once: Unified, Real-Time Object Detection:

YOLO9000 Better, Faster, Stronger:

G-CNN: an Iterative Grid Based Object Detector :

R-FCN: Object Detection via Region-based Fully Convolutional Networks:

R-FCN-3000 :

DetNet: A Backbone network for Object :

Relation Networks for Object Detection :

RefineDet: Single-Shot Refinement Neural Network for Object Detection:

Bag of Freebies for Training Object Detection Neural Networks:

FCOS: Fully Convolutional One-Stage Object Detection:


Fully Convolutional Networks for Semantic Segmentation:

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs:

U-Net: Convolutional Networks for Biomedical Image Segmentation:

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs:

DeepLab v2: Rethinking Atrous Convolution for Semantic Image Segmentation:

DeepLab v3: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation:

UNet++: A Nested U-Net Architecture for Medical Image Segmentation :

FCIS:Fully Convolutional Instance-aware Semantic Segmentation:

PAN: Path Aggregation Network for Instance Segmentation:

Mask Scoring R-CNN:

YOLACT:Real-time Instance Segmentation:


DeepPose: Human Pose Estimation via Deep Neural Networks:

Stacked Hourglass Networks for Human Pose Estimation:

DensePose: Dense Human Pose Estimation In The Wild:


On-line Boosting and Vision:

Online Object Tracking: A Benchmark:

Transferring Rich Feature Hierarchies for Robust Visual Tracking:

Visual tracking with fully convolutional networks(FCNT):

Learning to Track at 100 FPS with Deep Regression Networks(GOTURN):

Fully-Convolutional Siamese Networks for Object Tracking :

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking:


MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks:

Bayesian Face Revisited: A Joint Formulation:

DeepID1: Deep Learning Face Representation from Predicting 10,000 Classes

DeepID2: Deep Learning Face Representation by Joint Identification-Verification

DeepID2+: Deeply learned face representations are sparse, selective, and robust:

DeepID3: Face Recognition with Very Deep Neural Networks

FaceNet: A Unified Embedding for Face Recognition and Clustering:

Deep Face Recognition:

A Discriminative Feature Learning Approach for Deep Face Recognition :

SphereFace: Deep Hypersphere Embedding for Face Recognition:

ArcFace/InsightFace : Additive Angular Margin Loss for Deep Face Recognition :

MobileFaceNets: Efficient CNNs for Accurate RealTime Face Verification on Mobile Devices:

DocFace+: ID Document to Selfie* Matching:

Low-Resolution Face Recognition:

Accurate and Efficient Similarity Search for Large Scale Face Recognition:


CRNN: An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition:

Synthetic Data for Text Localisation in Natural Images:

Scene text detection via holistic, multi-channel prediction:

CPTN: Detecting Text in Natural Image with Connectionist Text Proposal Network:

SegLink: Detecting Oriented Text in Natural Images by Linking Segments:

Attention-based Extraction of Structured Information from Street View Imagery:

EAST: An Efficient and Accurate Scene Text Detector:

TextBoxes: A Fast Text Detector with a Single Deep Neural Network:

TextBoxes++: A Single-Shot Oriented Scene Text Detector:


Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network:

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification:

Co-Learning Feature Fusion Maps from PET-CTImages of Lung Cancer:

Lung Nodule Classification using Deep Local-Global Networks:

Gated-Dilated Networks for Lung Nodule Classification in CT scans:




Context-Aware Crowd Counting:




Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network :

Future Frame Prediction for Anomaly Detection – A New Baseline:

Real-world Anomaly Detection in Surveillance Videos:


PReLU:Delving Deep into Rectifiers:Surpassing Human-Level Performance on ImageNet Classification:


SELU: Self-Normalizing Neural Networks:


Higher Order Recurrent Neural Networks:

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