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🎨 🎨 深度学习 卷积神经网络教程 ：图像识别，目标检测，语义分割，实例分割，人脸识别，神经风格转换，GAN等🎨🎨 https://dataxujing.github.io/CNN-paper2/

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Welcome to CNN learning

徐静

HomePage: https://dataxujing.github.io/

关于CNN的基础知识及相关理论推导可以参考：https://dataxujing.github.io/深度学习之CNN/

目录

  • ResNet
  • Google Inception
  • DensenNet
  • SENet and ResNeXt
  • R-CNN, Selective Search, SPP-net
  • Fast R-CNN
  • Faster R-CNN
  • Light-Head R-CNN
  • Cascade R-CNN
  • SSD系列
  • Mask R-CNN
  • YOLO
  • Pelee
  • R-FCN
  • FPN
  • RetinaNet
  • MegDet
  • DetNet
  • ZSD
  • RFBNet
  • DeNet
  • 从MobileNet到ShuffleNet
  • 神经风格转换
  • 人脸识别
  • 图像分割
  • N种卷积
  • GANs
  • anchor free

常用图像分类CNN结构

  • ConvNet：卷积神经网络名称

  • ImageNet top1 acc：该网络在ImageNet上Top1 最佳准确率

  • ImageNet top5 acc：该网络在ImageNet上Top5 最佳准确率

  • Published In：发表源（期刊/会议/arXiv）

ConvNet ImageNet top1 acc ImageNet top5 acc Published In
Vgg 76.3 93.2 ICLR2015
GoogleNet - 93.33 CVPR2015
PReLU-nets - 95.06 ICCV2015
ResNet - 96.43 CVPR2015
PreActResNet 79.9 95.2 CVPR2016
Inceptionv3 82.8 96.42 CVPR2016
Inceptionv4 82.3 96.2 AAAI2016
Inception-ResNet-v2 82.4 96.3 AAAI2016
Inceptionv4 + Inception-ResNet-v2 83.5 96.92 AAAI2016
RiR - - ICLR Workshop2016
Stochastic Depth ResNet 78.02 - ECCV2016
WRN 78.1 94.21 BMVC2016
SqueezeNet 60.4 82.5 arXiv2017(rejected by ICLR2017)
GeNet 72.13 90.26 ICCV2017
MetaQNN - - ICLR2017
PyramidNet 80.8 95.3 CVPR2017
DenseNet 79.2 94.71 ECCV2017
FractalNet 75.8 92.61 ICLR2017
ResNext - 96.97 CVPR2017
IGCV1 73.05 91.08 ICCV2017
Residual Attention Network 80.5 95.2 CVPR2017
Xception 79 94.5 CVPR2017
MobileNet 70.6 - arXiv2017
PolyNet 82.64 96.55 CVPR2017
DPN 79 94.5 NIPS2017
Block-QNN 77.4 93.54 CVPR2018
CRU-Net 79.7 94.7 IJCAI2018
ShuffleNet 75.3 - CVPR2018
CondenseNet 73.8 91.7 CVPR2018
NasNet 82.7 96.2 CVPR2018
MobileNetV2 74.7 - CVPR2018
IGCV2 70.07 - CVPR2018
hier 79.7 94.8 ICLR2018
PNasNet 82.9 96.2 ECCV2018
AmoebaNet 83.9 96.6 arXiv2018
SENet - 97.749 CVPR2018
ShuffleNetV2 81.44 - ECCV2018
IGCV3 72.2 - BMVC2018
MnasNet 76.13 92.85 arXiv2018

from: https://github.com/weiaicunzai/awesome-image-classification

关于LeNet-5,AlexNet,VGG16,VGG19这些网络结构我们在https://dataxujing.github.io/深度学习之CNN/中已经详细的解释，并且本教程中涉及的网路结构像ResNet,NIN,Inception,YOLO等也做了详细解释。本教程是对这些网络结构更详细的讨论。

目标检测资源

来源：Object Detectionhttps://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#blogs

Method backbone test size VOC2007 VOC2010 VOC2012 ILSVRC 2013 MSCOCO 2015 Speed
OverFeat           24.3%    
R-CNN AlexNet   58.5% 53.7% 53.3% 31.4%    
R-CNN VGG16   66.0%          
SPP_net ZF-5   54.2%     31.84%    
DeepID-Net     64.1%     50.3%    
NoC 73.3%   68.8%          
Fast-RCNN VGG16   70.0% 68.8% 68.4%   19.7%(@[0.5-0.95]), 35.9%(@0.5)  
MR-CNN 78.2%   73.9%          
Faster-RCNN VGG16   78.8%   75.9%   21.9%(@[0.5-0.95]), 42.7%(@0.5) 198ms
Faster-RCNN ResNet101   85.6%   83.8%   37.4%(@[0.5-0.95]), 59.0%(@0.5)  
YOLO     63.4%   57.9%     45 fps
YOLO VGG-16     66.4%         21 fps
YOLOv2   448x448 78.6%   73.4%   21.6%(@[0.5-0.95]), 44.0%(@0.5) 40 fps
SSD VGG16 300x300 77.2%   75.8%   25.1%(@[0.5-0.95]), 43.1%(@0.5) 46 fps
SSD VGG16 512x512 79.8%   78.5%   28.8%(@[0.5-0.95]), 48.5%(@0.5) 19 fps
SSD ResNet101 300x300         28.0%(@[0.5-0.95]) 16 fps
SSD ResNet101 512x512         31.2%(@[0.5-0.95]) 8 fps
DSSD ResNet101 300x300         28.0%(@[0.5-0.95]) 8 fps
DSSD ResNet101 500x500         33.2%(@[0.5-0.95]) 6 fps
ION     79.2%   76.4%      
CRAFT     75.7%   71.3% 48.5%    
OHEM     78.9%   76.3%   25.5%(@[0.5-0.95]), 45.9%(@0.5)  
R-FCN ResNet50   77.4%         0.12sec(K40), 0.09sec(TitianX)
R-FCN ResNet101   79.5%         0.17sec(K40), 0.12sec(TitianX)
R-FCN(ms train) ResNet101   83.6%   82.0%   31.5%(@[0.5-0.95]), 53.2%(@0.5)  
PVANet 9.0     84.9%   84.2%     750ms(CPU), 46ms(TitianX)
RetinaNet ResNet101-FPN              
Light-Head R-CNN Xception* 800/1200         31.5%@[0.5:0.95] 95 fps
Light-Head R-CNN Xception* 700/1100         30.7%@[0.5:0.95] 102 fps

Deep Neural Networks for Object Detection

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

Fast R-CNN

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

Faster R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

R-CNN minus R

Faster R-CNN in MXNet with distributed implementation and data parallelization

Contextual Priming and Feedback for Faster R-CNN

An Implementation of Faster RCNN with Study for Region Sampling

Interpretable R-CNN

Light-Head R-CNN Light-Head R-CNN: In Defense of Two-Stage Object Detector

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

MultiBox Scalable Object Detection using Deep Neural Networks

Scalable, High-Quality Object Detection

SPP-Net Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

Object Detectors Emerge in Deep Scene CNNs

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

Object Detection Networks on Convolutional Feature Maps

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

DeepBox: Learning Objectness with Convolutional Networks

MR-CNN Object detection via a multi-region & semantic segmentation-aware CNN model

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

darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

Start Training YOLO with Our Own Data

YOLO: Core ML versus MPSNNGraph

TensorFlow YOLO object detection on Android

Computer Vision in iOS – Object Detection

YOLOv2 YOLO9000: Better, Faster, Stronger

darknet_scripts

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

LightNet: Bringing pjreddie’s DarkNet out of the shadows

YOLO v2 Bounding Box Tool

YOLOv3 YOLOv3: An Incremental Improvement

YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

DenseBox DenseBox: Unifying Landmark Localization with End to End Object Detection

SSD SSD: Single Shot MultiBox Detector

What’s the diffience in performance between this new code you pushed and the previous code? #327

DSSD DSSD : Deconvolutional Single Shot Detector

Enhancement of SSD by concatenating feature maps for object detection

Context-aware Single-Shot Detector

keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)

Feature-Fused SSD: Fast Detection for Small Objects

FSSD FSSD: Feature Fusion Single Shot Multibox Detector

Weaving Multi-scale Context for Single Shot Detector

ESSD Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

Inside-Outside Net (ION) Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Adaptive Object Detection Using Adjacency and Zoom Prediction

G-CNN: an Iterative Grid Based Object Detector

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

We don’t need no bounding-boxes: Training object class detectors using only human verification

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

A MultiPath Network for Object Detection

CRAFT CRAFT Objects from Images

OHEM Training Region-based Object Detectors with Online Hard Example Mining

S-OHEM: Stratified Online Hard Example Mining for Object Detection

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

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

R-FCN-3000 at 30fps: Decoupling Detection and Classification

Recycle deep features for better object detection

MS-CNN A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

Multi-stage Object Detection with Group Recursive Learning

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

PVANET PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

GBD-Net Gated Bi-directional CNN for Object Detection

Crafting GBD-Net for Object Detection

StuffNet: Using ‘Stuff’ to Improve Object Detection

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

Hierarchical Object Detection with Deep Reinforcement Learning

hierarchical-object-detection-with-deep-reinforcement-learning

Learning to detect and localize many objects from few examples

Speed/accuracy trade-offs for modern convolutional object detectors

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

Feature Pyramid Network (FPN) Feature Pyramid Networks for Object Detection

Action-Driven Object Detection with Top-Down Visual Attentions

Beyond Skip Connections: Top-Down Modulation for Object Detection

Wide-Residual-Inception Networks for Real-time Object Detection

Attentional Network for Visual Object Detection

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries

Spatial Memory for Context Reasoning in Object Detection

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

Point Linking Network for Object Detection

Perceptual Generative Adversarial Networks for Small Object Detection

Few-shot Object Detection

Yes-Net: An effective Detector Based on Global Information

SMC Faster R-CNN: Toward a scene-specialized multi-object detector

Towards lightweight convolutional neural networks for object detection

RON: Reverse Connection with Objectness Prior Networks for Object Detection

Mimicking Very Efficient Network for Object Detection

Residual Features and Unified Prediction Network for Single Stage Detection

Deformable Part-based Fully Convolutional Network for Object Detection

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

Recurrent Scale Approximation for Object Detection in CNN

DSOD DSOD: Learning Deeply Supervised Object Detectors from Scratch

Object Detection from Scratch with Deep Supervision

RetinaNet Focal Loss for Dense Object Detection

Focal Loss Dense Detector for Vehicle Surveillance

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

Incremental Learning of Object Detectors without Catastrophic Forgetting

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

Dynamic Zoom-in Network for Fast Object Detection in Large Images

Zero-Annotation Object Detection with Web Knowledge Transfer

MegDet MegDet: A Large Mini-Batch Object Detector

Single-Shot Refinement Neural Network for Object Detection

Receptive Field Block Net for Accurate and Fast Object Detection

An Analysis of Scale Invariance in Object Detection - SNIP

Feature Selective Networks for Object Detection

Learning a Rotation Invariant Detector with Rotatable Bounding Box

Scalable Object Detection for Stylized Objects

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

Deep Regionlets for Object Detection

Training and Testing Object Detectors with Virtual Images

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

  • keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
  • arxiv: https://arxiv.org/abs/1712.08832

Spot the Difference by Object Detection

Localization-Aware Active Learning for Object Detection

Object Detection with Mask-based Feature Encoding

LSTD: A Low-Shot Transfer Detector for Object Detection

Domain Adaptive Faster R-CNN for Object Detection in the Wild

Pseudo Mask Augmented Object Detection

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Decoupled-Classification-Refinement

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

Learning Region Features for Object Detection

Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

Object Detection for Comics using Manga109 Annotations

Task-Driven Super Resolution: Object Detection in Low-resolution Images

Transferring Common-Sense Knowledge for Object Detection

Multi-scale Location-aware Kernel Representation for Object Detection

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

DetNet: A Backbone network for Object Detection

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

AdvDetPatch: Attacking Object Detectors with Adversarial Patches

Attacking Object Detectors via Imperceptible Patches on Background

Physical Adversarial Examples for Object Detectors

Quantization Mimic: Towards Very Tiny CNN for Object Detection

Object detection at 200 Frames Per Second

Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

SNIPER: Efficient Multi-Scale Training

Soft Sampling for Robust Object Detection

MetaAnchor: Learning to Detect Objects with Customized Anchors

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Auto-Context R-CNN

Pooling Pyramid Network for Object Detection

Modeling Visual Context is Key to Augmenting Object Detection Datasets

Dual Refinement Network for Single-Shot Object Detection

Acquisition of Localization Confidence for Accurate Object Detection

CornerNet: Detecting Objects as Paired Keypoints

Unsupervised Hard Example Mining from Videos for Improved Object Detection

SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

A Survey of Modern Object Detection Literature using Deep Learning

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

Deep Feature Pyramid Reconfiguration for Object Detection

MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Deep Learning for Generic Object Detection: A Survey

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples

ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch

Fast and accurate object detection in high resolution 4K and 8K video using GPUs

  • intro: Best Paper Finalist at IEEE High Performance Extreme Computing Conference (HPEC) 2018
  • intro: Carnegie Mellon University
  • arxiv: https://arxiv.org/abs/1810.10551

Hybrid Knowledge Routed Modules for Large-scale Object Detection

Gradient Harmonized Single-stage Detector

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

BAN: Focusing on Boundary Context for Object Detection

Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector

R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy

DeRPN: Taking a further step toward more general object detection

Fast Efficient Object Detection Using Selective Attention

Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects

Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects

Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

Grid R-CNN

Transferable Adversarial Attacks for Image and Video Object Detection

Anchor Box Optimization for Object Detection

AutoFocus: Efficient Multi-Scale Inference

Few-shot Object Detection via Feature Reweighting

Learning Efficient Detector with Semi-supervised Adaptive Distillation

Scale-Aware Trident Networks for Object Detection

Region Proposal by Guided Anchoring

Consistent Optimization for Single-Shot Object Detection

Bottom-up Object Detection by Grouping Extreme and Center Points

A Single-shot Object Detector with Feature Aggragation and Enhancement

Bag of Freebies for Training Object Detection Neural Networks

Non-Maximum Suppression (NMS) End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression

A convnet for non-maximum suppression

Soft-NMS – Improving Object Detection With One Line of Code

Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection

Learning non-maximum suppression

Relation Networks for Object Detection

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples

Adversarial Examples that Fool Detectors

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

Weakly Supervised Object Detection Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

Weakly supervised object detection using pseudo-strong labels

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Video Object Detection Learning Object Class Detectors from Weakly Annotated Video

Analysing domain shift factors between videos and images for object detection

Video Object Recognition

Deep Learning for Saliency Prediction in Natural Video

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

Object Detection from Video Tubelets with Convolutional Neural Networks

Object Detection in Videos with Tubelets and Multi-context Cues

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

CNN Based Object Detection in Large Video Images

Object Detection in Videos with Tubelet Proposal Networks

Flow-Guided Feature Aggregation for Video Object Detection

Video Object Detection using Faster R-CNN

Improving Context Modeling for Video Object Detection and Tracking

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

Mobile Video Object Detection with Temporally-Aware Feature Maps

Towards High Performance Video Object Detection

Impression Network for Video Object Detection

Spatial-Temporal Memory Networks for Video Object Detection

3D-DETNet: a Single Stage Video-Based Vehicle Detector

Object Detection in Videos by Short and Long Range Object Linking

Object Detection in Video with Spatiotemporal Sampling Networks

Towards High Performance Video Object Detection for Mobiles

Optimizing Video Object Detection via a Scale-Time Lattice

Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

Fast Object Detection in Compressed Video

Tube-CNN: Modeling temporal evolution of appearance for object detection in video

AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling

Object Detection on Mobile Devices Pelee: A Real-Time Object Detection System on Mobile Devices

Object Detection in 3D Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Focal Loss in 3D Object Detection

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

3D Backbone Network for 3D Object Detection

Object Detection on RGB-D Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

Cross-Modal Attentional Context Learning for RGB-D Object Detection

Zero-Shot Object Detection Zero-Shot Detection

Zero-Shot Object Detection

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

Zero-Shot Object Detection by Hybrid Region Embedding

Salient Object Detection This task involves predicting the salient regions of an image given by human eye fixations.

Large-scale optimization of hierarchical features for saliency prediction in natural images

Predicting Eye Fixations using Convolutional Neural Networks

Saliency Detection by Multi-Context Deep Learning

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

Shallow and Deep Convolutional Networks for Saliency Prediction

Recurrent Attentional Networks for Saliency Detection

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

Salient Object Subitizing

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

A Deep Multi-Level Network for Saliency Prediction

Visual Saliency Detection Based on Multiscale Deep CNN Features

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

Deeply supervised salient object detection with short connections

Weakly Supervised Top-down Salient Object Detection

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network

Saliency Detection by Forward and Backward Cues in Deep-CNNs

Supervised Adversarial Networks for Image Saliency Detection

Group-wise Deep Co-saliency Detection

Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

Learning Uncertain Convolutional Features for Accurate Saliency Detection

Deep Edge-Aware Saliency Detection

Self-explanatory Deep Salient Object Detection

PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection

DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets

Recurrently Aggregating Deep Features for Salient Object Detection

Deep saliency: What is learnt by a deep network about saliency?

Contrast-Oriented Deep Neural Networks for Salient Object Detection

Salient Object Detection by Lossless Feature Reflection

HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

Video Saliency Detection Deep Learning For Video Saliency Detection

Video Salient Object Detection Using Spatiotemporal Deep Features

Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Visual Relationship Detection Visual Relationship Detection with Language Priors

ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning

Visual Translation Embedding Network for Visual Relation Detection

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

Detecting Visual Relationships with Deep Relational Networks

Identifying Spatial Relations in Images using Convolutional Neural Networks

PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN

Natural Language Guided Visual Relationship Detection

Detecting Visual Relationships Using Box Attention

Google AI Open Images - Visual Relationship Track

Context-Dependent Diffusion Network for Visual Relationship Detection

A Problem Reduction Approach for Visual Relationships Detection

Face Deteciton Multi-view Face Detection Using Deep Convolutional Neural Networks

From Facial Parts Responses to Face Detection: A Deep Learning Approach

Compact Convolutional Neural Network Cascade for Face Detection

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Towards a Deep Learning Framework for Unconstrained Face Detection

Supervised Transformer Network for Efficient Face Detection

UnitBox: An Advanced Object Detection Network

Bootstrapping Face Detection with Hard Negative Examples

Grid Loss: Detecting Occluded Faces

A Multi-Scale Cascade Fully Convolutional Network Face Detector

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

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

Face Detection using Deep Learning: An Improved Faster RCNN Approach

Faceness-Net: Face Detection through Deep Facial Part Responses

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”

End-To-End Face Detection and Recognition

Face R-CNN

Face Detection through Scale-Friendly Deep Convolutional Networks

Scale-Aware Face Detection

Detecting Faces Using Inside Cascaded Contextual CNN

Multi-Branch Fully Convolutional Network for Face Detection

SSH: Single Stage Headless Face Detector

Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

S3FD: Single Shot Scale-invariant Face Detector

Detecting Faces Using Region-based Fully Convolutional Networks

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Face Attention Network: An effective Face Detector for the Occluded Faces

Feature Agglomeration Networks for Single Stage Face Detection

Face Detection Using Improved Faster RCNN

PyramidBox: A Context-assisted Single Shot Face Detector

A Fast Face Detection Method via Convolutional Neural Network

Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy

Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

SFace: An Efficient Network for Face Detection in Large Scale Variations

Survey of Face Detection on Low-quality Images

Anchor Cascade for Efficient Face Detection

Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization

Selective Refinement Network for High Performance Face Detection

DSFD: Dual Shot Face Detector

Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision

FA-RPN: Floating Region Proposals for Face Detection

Robust and High Performance Face Detector

DAFE-FD: Density Aware Feature Enrichment for Face Detection

Improved Selective Refinement Network for Face Detection

Revisiting a single-stage method for face detection

Detect Small Faces Finding Tiny Faces

Detecting and counting tiny faces

Seeing Small Faces from Robust Anchor’s Perspective

Face-MagNet: Magnifying Feature Maps to Detect Small Faces

Robust Face Detection via Learning Small Faces on Hard Images

SFA: Small Faces Attention Face Detector

Person Head Detection Context-aware CNNs for person head detection

Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture

A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

FCHD: A fast and accurate head detector

Pedestrian Detection / People Detection Pedestrian Detection aided by Deep Learning Semantic Tasks

Deep Learning Strong Parts for Pedestrian Detection

Taking a Deeper Look at Pedestrians

Convolutional Channel Features

End-to-end people detection in crowded scenes

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

Deep convolutional neural networks for pedestrian detection

Scale-aware Fast R-CNN for Pedestrian Detection

New algorithm improves speed and accuracy of pedestrian detection

Pushing the Limits of Deep CNNs for Pedestrian Detection

  • intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
  • arxiv: http://arxiv.org/abs/1603.04525

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

Is Faster R-CNN Doing Well for Pedestrian Detection?

Unsupervised Deep Domain Adaptation for Pedestrian Detection

Reduced Memory Region Based Deep Convolutional Neural Network Detection

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

Detecting People in Artwork with CNNs

Multispectral Deep Neural Networks for Pedestrian Detection

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Deep Multi-camera People Detection

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

What Can Help Pedestrian Detection?

Illuminating Pedestrians via Simultaneous Detection & Segmentation

Rotational Rectification Network for Robust Pedestrian Detection

STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos

Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy

Repulsion Loss: Detecting Pedestrians in a Crowd

Aggregated Channels Network for Real-Time Pedestrian Detection

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

PCN: Part and Context Information for Pedestrian Detection with CNNs

Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

Pedestrian Detection with Autoregressive Network Phases

The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection

Vehicle Detection DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

Evolving Boxes for fast Vehicle Detection

Fine-Grained Car Detection for Visual Census Estimation

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data

Domain Randomization for Scene-Specific Car Detection and Pose Estimation

ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery

Traffic-Sign Detection Traffic-Sign Detection and Classification in the Wild

Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

Detecting Small Signs from Large Images

Localized Traffic Sign Detection with Multi-scale Deconvolution Networks

Detecting Traffic Lights by Single Shot Detection

A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection

Skeleton Detection Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

Fruit Detection Deep Fruit Detection in Orchards

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Shadow Detection Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network

A+D-Net: Shadow Detection with Adversarial Shadow Attenuation

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

Direction-aware Spatial Context Features for Shadow Detection

Direction-aware Spatial Context Features for Shadow Detection and Removal

Others Detection Deep Deformation Network for Object Landmark Localization

Fashion Landmark Detection in the Wild

Deep Learning for Fast and Accurate Fashion Item Detection

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

Associative Embedding:End-to-End Learning for Joint Detection and Grouping

Deep Cuboid Detection: Beyond 2D Bounding Boxes

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Deep Learning Logo Detection with Data Expansion by Synthesising Context

Scalable Deep Learning Logo Detection

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

Automatic Handgun Detection Alarm in Videos Using Deep Learning

Objects as context for part detection

Using Deep Networks for Drone Detection

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

Target Driven Instance Detection

DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants

ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos

Deep Learning Object Detection Methods for Ecological Camera Trap Data

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

Towards End-to-End Lane Detection: an Instance Segmentation Approach

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Densely Supervised Grasp Detector (DSGD)

Object Proposal DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

Scale-aware Pixel-wise Object Proposal Networks

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

Learning to Segment Object Proposals via Recursive Neural Networks

Learning Detection with Diverse Proposals

  • intro: CVPR 2017

keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse P roposals (LDDP)

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

Improving Small Object Proposals for Company Logo Detection

Open Logo Detection Challenge

AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects

Localization Beyond Bounding Boxes: Precise Localization of Objects in Images

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

Weakly Supervised Object Localization Using Size Estimates

Active Object Localization with Deep Reinforcement Learning

Localizing objects using referring expressions

LocNet: Improving Localization Accuracy for Object Detection

Learning Deep Features for Discriminative Localization

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

Ensemble of Part Detectors for Simultaneous Classification and Localization

STNet: Selective Tuning of Convolutional Networks for Object Localization

Soft Proposal Networks for Weakly Supervised Object Localization

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

Tutorials / Talks Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

Towards Good Practices for Recognition & Detection

Work in progress: Improving object detection and instance segmentation for small objects

Object Detection with Deep Learning: A Review

Projects Detectron

TensorBox: a simple framework for training neural networks to detect objects in images

  • intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
  • github: https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch

Using DIGITS to train an Object Detection network

FCN-MultiBox Detector

KittiBox: A car detection model implemented in Tensorflow.

Deformable Convolutional Networks + MST + Soft-NMS

How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow

Metrics for object detection

MobileNetv2-SSDLite

Leaderboard Detection Results: VOC2012

BeaverDam: Video annotation tool for deep learning training labels

Convolutional Neural Networks for Object Detection

Introducing automatic object detection to visual search (Pinterest)

Deep Learning for Object Detection with DIGITS

Analyzing The Papers Behind Facebook’s Computer Vision Approach

Easily Create High Quality Object Detectors with Deep Learning

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

Object Detection in Satellite Imagery, a Low Overhead Approach

You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

Faster R-CNN Pedestrian and Car Detection

Small U-Net for vehicle detection

Region of interest pooling explained

Supercharge your Computer Vision models with the TensorFlow Object Detection API

Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning

One-shot object detection

An overview of object detection: one-stage methods

deep learning object detection

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