dafe：Object Detection(目标检测神文)---3

 

---

Object Detection on Mobile Devices

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

• intro: ICLR 2018 workshop track
• intro: based on the SSD
• arxiv: https://arxiv.org/abs/1804.06882
• github: https://github.com/Robert-JunWang/Pelee

---

Object Detection in 3D

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

• arxiv: https://arxiv.org/abs/1609.06666

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

• intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
• arxiv: https://arxiv.org/abs/1803.06199

Focal Loss in 3D Object Detection

• arxiv: https://arxiv.org/abs/1809.06065
• github: https://github.com/pyun-ram/FL3D

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

• intro: AAAI 2019
• arxiv: https://arxiv.org/abs/1901.02237

3D Backbone Network for 3D Object Detection

• arxiv: https://arxiv.org/abs/1901.08373

---

Object Detection on RGB-D

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

• arxiv: http://arxiv.org/abs/1407.5736

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

• intro: CVPR 2016
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html

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

• arxiv: https://arxiv.org/abs/1703.03347

---

Zero-Shot Object Detection

Zero-Shot Detection

• intro: Australian National University
• keywords: YOLO
• arxiv: https://arxiv.org/abs/1803.07113

Zero-Shot Object Detection

• arxiv: https://arxiv.org/abs/1804.04340

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

• intro: Australian National University
• arxiv: https://arxiv.org/abs/1803.06049

Zero-Shot Object Detection by Hybrid Region Embedding

• intro: Middle East Technical University & Hacettepe University
• arxiv: https://arxiv.org/abs/1805.06157

---

Salient Object Detection

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

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

• page: http://i.cs.hku.hk/~yzyu/vision.html

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

• paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf

Predicting Eye Fixations using Convolutional Neural Networks

• paper: http://www.escience.cn/system/file?fileId=72648

Saliency Detection by Multi-Context Deep Learning

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf

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

• arxiv: http://arxiv.org/abs/1510.05484

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

• paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

Shallow and Deep Convolutional Networks for Saliency Prediction

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1603.00845
• github: https://github.com/imatge-upc/saliency-2016-cvpr

Recurrent Attentional Networks for Saliency Detection

• intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
• arxiv: http://arxiv.org/abs/1604.03227

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

• arxiv: http://arxiv.org/abs/1607.04730

---

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

• intro: CVPR 2016
• project page: http://cs-people.bu.edu/jmzhang/sod.html
• paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
• github: https://github.com/jimmie33/SOD
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf

Salient Object Subitizing

• intro: CVPR 2015
• intro: predicting the existence and the number of salient objects in an image using holistic cues
• project page: http://cs-people.bu.edu/jmzhang/sos.html
• arxiv: http://arxiv.org/abs/1607.07525
• paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

• intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
• arxiv: http://arxiv.org/abs/1608.05177

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

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.05186

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

• arxiv: http://arxiv.org/abs/1608.08029

A Deep Multi-Level Network for Saliency Prediction

• arxiv: http://arxiv.org/abs/1609.01064

Visual Saliency Detection Based on Multiscale Deep CNN Features

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1609.02077

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

• intro: DSCLRCN
• arxiv: https://arxiv.org/abs/1610.01708

Deeply supervised salient object detection with short connections

• intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
• arxiv: https://arxiv.org/abs/1611.04849
• github(official, Caffe): https://github.com/Andrew-Qibin/DSS
• github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection

Weakly Supervised Top-down Salient Object Detection

• intro: Nanyang Technological University
• arxiv: https://arxiv.org/abs/1611.05345

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

• project page: https://imatge-upc.github.io/saliency-salgan-2017/
• arxiv: https://arxiv.org/abs/1701.01081

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

• arxiv: https://arxiv.org/abs/1702.00372

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

• arxiv: https://arxiv.org/abs/1702.00615

Saliency Detection by Forward and Backward Cues in Deep-CNNs

• arxiv: https://arxiv.org/abs/1703.00152

Supervised Adversarial Networks for Image Saliency Detection

• arxiv: https://arxiv.org/abs/1704.07242

Group-wise Deep Co-saliency Detection

• arxiv: https://arxiv.org/abs/1707.07381

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

• intro: University of Maryland College Park & eBay Inc
• arxiv: https://arxiv.org/abs/1708.00079

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

• intro: ICCV 2017
• arixv: https://arxiv.org/abs/1708.02001

Learning Uncertain Convolutional Features for Accurate Saliency Detection

• intro: Accepted as a poster in ICCV 2017
• arxiv: https://arxiv.org/abs/1708.02031

Deep Edge-Aware Saliency Detection

• arxiv: https://arxiv.org/abs/1708.04366

Self-explanatory Deep Salient Object Detection

• intro: National University of Defense Technology, China & National University of Singapore
• arxiv: https://arxiv.org/abs/1708.05595

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

• arxiv: https://arxiv.org/abs/1708.06433

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

• arxiv: https://arxiv.org/abs/1709.02495

Recurrently Aggregating Deep Features for Salient Object Detection

• intro: AAAI 2018
• paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281

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

• intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
• arxiv: https://arxiv.org/abs/1801.04261

Contrast-Oriented Deep Neural Networks for Salient Object Detection

• intro: TNNLS
• arxiv: https://arxiv.org/abs/1803.11395

Salient Object Detection by Lossless Feature Reflection

• intro: IJCAI 2018
• arxiv: https://arxiv.org/abs/1802.06527

HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

• arxiv: https://arxiv.org/abs/1804.05142

---

Video Saliency Detection

Deep Learning For Video Saliency Detection

• arxiv: https://arxiv.org/abs/1702.00871

Video Salient Object Detection Using Spatiotemporal Deep Features

• arxiv: https://arxiv.org/abs/1708.01447

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

• arxiv: https://arxiv.org/abs/1709.06316

---

Visual Relationship Detection

Visual Relationship Detection with Language Priors

• intro: ECCV 2016 oral
• paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
• github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection

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 Structure (VPRS)
• arxiv: https://arxiv.org/abs/1702.07191

Visual Translation Embedding Network for Visual Relation Detection

• arxiv: https://www.arxiv.org/abs/1702.08319

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

• intro: CVPR 2017 spotlight paper
• arxiv: https://arxiv.org/abs/1703.03054

Detecting Visual Relationships with Deep Relational Networks

• intro: CVPR 2017 oral. The Chinese University of Hong Kong
• arxiv: https://arxiv.org/abs/1704.03114

Identifying Spatial Relations in Images using Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1706.04215

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

• intro: ICCV
• arxiv: https://arxiv.org/abs/1708.01956

Natural Language Guided Visual Relationship Detection

• arxiv: https://arxiv.org/abs/1711.06032

Detecting Visual Relationships Using Box Attention

• intro: Google AI & IST Austria
• arxiv: https://arxiv.org/abs/1807.02136

Google AI Open Images - Visual Relationship Track

• intro: Detect pairs of objects in particular relationships
• kaggle: https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track

Context-Dependent Diffusion Network for Visual Relationship Detection

• intro: 2018 ACM Multimedia Conference
• arxiv: https://arxiv.org/abs/1809.06213

A Problem Reduction Approach for Visual Relationships Detection

• intro: ECCV 2018 Workshop
• arxiv: https://arxiv.org/abs/1809.09828

---

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

• intro: Yahoo
• arxiv: http://arxiv.org/abs/1502.02766
• github: https://github.com/guoyilin/FaceDetection_CNN

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

• intro: ICCV 2015. CUHK
• project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
• arxiv: https://arxiv.org/abs/1509.06451
• paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_From_Facial_Parts_ICCV_2015_paper.pdf

Compact Convolutional Neural Network Cascade for Face Detection

• arxiv: http://arxiv.org/abs/1508.01292
• github: https://github.com/Bkmz21/FD-Evaluation
• github: https://github.com/Bkmz21/CompactCNNCascade

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

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1606.00850
• github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D

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

• intro: CMU
• arxiv: https://arxiv.org/abs/1606.05413

Towards a Deep Learning Framework for Unconstrained Face Detection

• intro: overlap with CMS-RCNN
• arxiv: https://arxiv.org/abs/1612.05322

Supervised Transformer Network for Efficient Face Detection

• arxiv: http://arxiv.org/abs/1607.05477

UnitBox: An Advanced Object Detection Network

• intro: ACM MM 2016
• keywords: IOULoss
• arxiv: http://arxiv.org/abs/1608.01471

Bootstrapping Face Detection with Hard Negative Examples

• author: 万韶华 @ 小米.
• intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
• arxiv: http://arxiv.org/abs/1608.02236

Grid Loss: Detecting Occluded Faces

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1609.00129
• paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
• poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf

A Multi-Scale Cascade Fully Convolutional Network Face Detector

• intro: ICPR 2016
• arxiv: http://arxiv.org/abs/1609.03536

---

MTCNN

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

• project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
• arxiv: https://arxiv.org/abs/1604.02878
• github(official, Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment
• github: https://github.com/pangyupo/mxnet_mtcnn_face_detection
• github: https://github.com/DaFuCoding/MTCNN_Caffe
• github(MXNet): https://github.com/Seanlinx/mtcnn
• github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
• github(Caffe): https://github.com/foreverYoungGitHub/MTCNN
• github: https://github.com/CongWeilin/mtcnn-caffe
• github(OpenCV+OpenBlas): https://github.com/AlphaQi/MTCNN-light
• github(Tensorflow+golang): https://github.com/jdeng/goface

Face Detection using Deep Learning: An Improved Faster RCNN Approach

• intro: DeepIR Inc
• arxiv: https://arxiv.org/abs/1701.08289

Faceness-Net: Face Detection through Deep Facial Part Responses

• intro: An extended version of ICCV 2015 paper
• arxiv: https://arxiv.org/abs/1701.08393

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

• intro: CVPR 2017. MP-RCNN, MP-RPN
• arxiv: https://arxiv.org/abs/1703.09145

End-To-End Face Detection and Recognition

• arxiv: https://arxiv.org/abs/1703.10818

Face R-CNN

• arxiv: https://arxiv.org/abs/1706.01061

Face Detection through Scale-Friendly Deep Convolutional Networks

• arxiv: https://arxiv.org/abs/1706.02863

Scale-Aware Face Detection

• intro: CVPR 2017. SenseTime & Tsinghua University
• arxiv: https://arxiv.org/abs/1706.09876

Detecting Faces Using Inside Cascaded Contextual CNN

• intro: CVPR 2017. Tencent AI Lab & SenseTime
• paper: http://ai.tencent.com/ailab/media/publications/Detecting_Faces_Using_Inside_Cascaded_Contextual_CNN.pdf

Multi-Branch Fully Convolutional Network for Face Detection

• arxiv: https://arxiv.org/abs/1707.06330

SSH: Single Stage Headless Face Detector

• intro: ICCV 2017. University of Maryland
• arxiv: https://arxiv.org/abs/1708.03979
• github(official, Caffe): https://github.com/mahyarnajibi/SSH

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

• arxiv: https://arxiv.org/abs/1708.04370

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

• intro: IJCB 2017
• keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
• intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
• arxiv: https://arxiv.org/abs/1708.05234
• github(official): https://github.com/sfzhang15/FaceBoxes
• github(Caffe): https://github.com/zeusees/FaceBoxes

S3FD: Single Shot Scale-invariant Face Detector

• intro: ICCV 2017. Chinese Academy of Sciences
• intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images
• arxiv: https://arxiv.org/abs/1708.05237
• github(Caffe, official): https://github.com/sfzhang15/SFD
• github: https://github.com//clcarwin/SFD_pytorch

Detecting Faces Using Region-based Fully Convolutional Networks

• arxiv: https://arxiv.org/abs/1709.05256

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

• arxiv: https://arxiv.org/abs/1709.07326

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

• arxiv: https://arxiv.org/abs/1711.07246

Feature Agglomeration Networks for Single Stage Face Detection

• arxiv: https://arxiv.org/abs/1712.00721

Face Detection Using Improved Faster RCNN

• intro: Huawei Cloud BU
• arxiv: https://arxiv.org/abs/1802.02142

PyramidBox: A Context-assisted Single Shot Face Detector

• intro: Baidu, Inc
• arxiv: https://arxiv.org/abs/1803.07737

A Fast Face Detection Method via Convolutional Neural Network

• intro: Neurocomputing
• arxiv: https://arxiv.org/abs/1803.10103

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

• intro: CVPR 2018. Beihang University & CUHK & Sensetime
• arxiv: https://arxiv.org/abs/1804.05197

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

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.06039
• github: https://github.com/Jack-CV/PCN

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

• intro: Beihang University & Megvii Inc. (Face++)
• arxiv: https://arxiv.org/abs/1804.06559

Survey of Face Detection on Low-quality Images

• arxiv: https://arxiv.org/abs/1804.07362

Anchor Cascade for Efficient Face Detection

• intro: The University of Sydney
• arxiv: https://arxiv.org/abs/1805.03363

Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization

• intro: IEEE MMSP
• arxiv: https://arxiv.org/abs/1805.12302

Selective Refinement Network for High Performance Face Detection

• https://arxiv.org/abs/1809.02693

DSFD: Dual Shot Face Detector

• arxiv：https://arxiv.org/abs/1810.10220

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

• arxiv：https://arxiv.org/abs/1811.08557

FA-RPN: Floating Region Proposals for Face Detection

• arxiv: https://arxiv.org/abs/1812.05586

Robust and High Performance Face Detector

https://arxiv.org/abs/1901.02350

DAFE-FD: Density Aware Feature Enrichment for Face Detection

• arxiv: https://arxiv.org/abs/1901.05375

Improved Selective Refinement Network for Face Detection

• intro: Chinese Academy of Sciences & JD AI Research
• arxiv: https://arxiv.org/abs/1901.06651

Revisiting a single-stage method for face detection

• arxiv: https://arxiv.org/abs/1902.01559

---

Detect Small Faces

Finding Tiny Faces

• intro: CVPR 2017. CMU
• project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html
• arxiv: https://arxiv.org/abs/1612.04402
• github(official, Matlab): https://github.com/peiyunh/tiny
• github(inference-only): https://github.com/chinakook/hr101_mxnet
• github: https://github.com/cydonia999/Tiny_Faces_in_Tensorflow

Detecting and counting tiny faces

• intro: ENS Paris-Saclay. ExtendedTinyFaces
• intro: Detecting and counting small objects - Analysis, review and application to counting
• arxiv: https://arxiv.org/abs/1801.06504
• github: https://github.com/alexattia/ExtendedTinyFaces

Seeing Small Faces from Robust Anchor’s Perspective

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.09058

Face-MagNet: Magnifying Feature Maps to Detect Small Faces

• intro: WACV 2018
• keywords: Face Magnifier Network (Face-MageNet)
• arxiv: https://arxiv.org/abs/1803.05258
• github: https://github.com/po0ya/face-magnet

Robust Face Detection via Learning Small Faces on Hard Images

• intro: Johns Hopkins University & Stanford University
• arxiv: https://arxiv.org/abs/1811.11662
• github: https://github.com/bairdzhang/smallhardface

SFA: Small Faces Attention Face Detector

• intro: Jilin University
• arxiv: https://arxiv.org/abs/1812.08402

---

Person Head Detection

Context-aware CNNs for person head detection

• intro: ICCV 2015
• project page: http://www.di.ens.fr/willow/research/headdetection/
• arxiv: http://arxiv.org/abs/1511.07917
• github: https://github.com/aosokin/cnn_head_detection

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

• arxiv: https://arxiv.org/abs/1803.09256

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

• https://arxiv.org/abs/1809.03336

FCHD: A fast and accurate head detector

• arxiv: https://arxiv.org/abs/1809.08766
• github(PyTorch, official): https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector

---

Pedestrian Detection / People Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

• intro: CVPR 2015
• project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
• arxiv: http://arxiv.org/abs/1412.0069

Deep Learning Strong Parts for Pedestrian Detection

• intro: ICCV 2015. CUHK. DeepParts
• intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf

Taking a Deeper Look at Pedestrians

• intro: CVPR 2015
• arxiv: https://arxiv.org/abs/1501.05790

Convolutional Channel Features

• intro: ICCV 2015
• arxiv: https://arxiv.org/abs/1504.07339
• github: https://github.com/byangderek/CCF

End-to-end people detection in crowded scenes

• arxiv: http://arxiv.org/abs/1506.04878
• github: https://github.com/Russell91/reinspect
• ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb
• youtube: https://www.youtube.com/watch?v=QeWl0h3kQ24

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

• intro: ICCV 2015
• arxiv: https://arxiv.org/abs/1507.05348

Deep convolutional neural networks for pedestrian detection

• arxiv: http://arxiv.org/abs/1510.03608
• github: https://github.com/DenisTome/DeepPed

Scale-aware Fast R-CNN for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1510.08160

New algorithm improves speed and accuracy of pedestrian detection

• blog: http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php

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

• arxiv: http://arxiv.org/abs/1607.04436

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

• arxiv: http://arxiv.org/abs/1607.04441

Is Faster R-CNN Doing Well for Pedestrian Detection?

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.07032
• github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

Unsupervised Deep Domain Adaptation for Pedestrian Detection

• intro: ECCV Workshop 2016
• arxiv: https://arxiv.org/abs/1802.03269

Reduced Memory Region Based Deep Convolutional Neural Network Detection

• intro: IEEE 2016 ICCE-Berlin
• arxiv: http://arxiv.org/abs/1609.02500

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

• arxiv: https://arxiv.org/abs/1610.03466

Detecting People in Artwork with CNNs

• intro: ECCV 2016 Workshops
• arxiv: https://arxiv.org/abs/1610.08871

Multispectral Deep Neural Networks for Pedestrian Detection

• intro: BMVC 2016 oral
• arxiv: https://arxiv.org/abs/1611.02644

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

• arxiv: https://arxiv.org/abs/1902.05291

Deep Multi-camera People Detection

• arxiv: https://arxiv.org/abs/1702.04593

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

• intro: CVPR 2017
• project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
• arxiv: https://arxiv.org/abs/1703.06283
• github(Tensorflow): https://github.com/huangshiyu13/RPNplus

What Can Help Pedestrian Detection?

• intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc.
• keywords: Faster R-CNN, HyperLearner
• arxiv: https://arxiv.org/abs/1705.02757
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf

Illuminating Pedestrians via Simultaneous Detection & Segmentation

• arxiv: https://arxiv.org/abs/1706.08564

Rotational Rectification Network for Robust Pedestrian Detection

• intro: CMU & Volvo Construction
• arxiv: https://arxiv.org/abs/1706.08917

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

• intro: The University of North Carolina at Chapel Hill
• arxiv: https://arxiv.org/abs/1707.09100

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

• arxiv: https://arxiv.org/abs/1709.00235

Repulsion Loss: Detecting Pedestrians in a Crowd

• arxiv: https://arxiv.org/abs/1711.07752

Aggregated Channels Network for Real-Time Pedestrian Detection

• arxiv: https://arxiv.org/abs/1801.00476

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

• intro: State Key Lab of CAD&CG, Zhejiang University
• arxiv: https://arxiv.org/abs/1803.05347

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

• arxiv: https://arxiv.org/abs/1804.00872

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

• arxiv: https://arxiv.org/abs/1804.02047

PCN: Part and Context Information for Pedestrian Detection with CNNs

• intro: British Machine Vision Conference(BMVC) 2017
• arxiv: https://arxiv.org/abs/1804.04483

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

• intro: ECCV 2018. Hikvision Research Institute
• arxiv: https://arxiv.org/abs/1807.01438

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

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1807.08407

Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1808.04818

Pedestrian Detection with Autoregressive Network Phases

• intro: Michigan State University
• arxiv: https://arxiv.org/abs/1812.00440

The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection

-arxiv: https://arxiv.org/abs/1901.02645

---

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.04564

Evolving Boxes for fast Vehicle Detection

• arxiv: https://arxiv.org/abs/1702.00254

Fine-Grained Car Detection for Visual Census Estimation

• intro: AAAI 2016
• arxiv: https://arxiv.org/abs/1709.02480

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

• intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
• arxiv: https://arxiv.org/abs/1804.00433

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

• intro: UC Berkeley
• arxiv: https://arxiv.org/abs/1808.08603

Domain Randomization for Scene-Specific Car Detection and Pose Estimation

• arxiv：https://arxiv.org/abs/1811.05939

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

• intro: ECCV 2018, UAVision 2018
• arxiv: https://arxiv.org/abs/1811.06318

---

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

• intro: CVPR 2016
• project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
• code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip

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

• intro: CVPR 2017 workshop
• paper: http://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf

Detecting Small Signs from Large Images

• intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
• arxiv: https://arxiv.org/abs/1706.08574

Localized Traffic Sign Detection with Multi-scale Deconvolution Networks

• arxiv: https://arxiv.org/abs/1804.10428

Detecting Traffic Lights by Single Shot Detection

• intro: ITSC 2018
• arxiv: https://arxiv.org/abs/1805.02523

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

• intro: IEEE 15th Conference on Computer and Robot Vision
• arxiv: https://arxiv.org/abs/1806.07987
• demo: https://www.youtube.com/watch?v=_YmogPzBXOw&feature=youtu.be

---

Skeleton Detection

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

• arxiv: http://arxiv.org/abs/1603.09446
• github: https://github.com/zeakey/DeepSkeleton

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

• arxiv: http://arxiv.org/abs/1609.03659

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

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1703.02243
• github: https://github.com/KevinKecc/SRN

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

• arxiv: https://arxiv.org/abs/1801.01849

---

Fruit Detection

Deep Fruit Detection in Orchards

• arxiv: https://arxiv.org/abs/1610.03677

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

• intro: The Journal of Field Robotics in May 2016
• project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
• arxiv: https://arxiv.org/abs/1610.08120

---

Shadow Detection

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

• arxiv: https://arxiv.org/abs/1709.09283

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

• arxiv: https://arxiv.org/abs/1712.01361

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

• arxiv: https://arxiv.org/abs/1712.02478

Direction-aware Spatial Context Features for Shadow Detection

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1712.04142

Direction-aware Spatial Context Features for Shadow Detection and Removal

• intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
• arxiv: https://arxiv.org/abs/1805.04635

---

Others Detection

Deep Deformation Network for Object Landmark Localization

• arxiv: http://arxiv.org/abs/1605.01014

Fashion Landmark Detection in the Wild

• intro: ECCV 2016
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
• arxiv: http://arxiv.org/abs/1608.03049
• github(Caffe): https://github.com/liuziwei7/fashion-landmarks

Deep Learning for Fast and Accurate Fashion Item Detection

• intro: Kuznech Inc.
• intro: MultiBox and Fast R-CNN
• paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep Learning for Fast and Accurate Fashion Item Detection.pdf

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

• github: https://github.com/geometalab/OSMDeepOD

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

• intro: IEEE SITIS 2016
• arxiv: https://arxiv.org/abs/1611.04357

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

• arxiv: https://arxiv.org/abs/1611.05424

Deep Cuboid Detection: Beyond 2D Bounding Boxes

• intro: CMU & Magic Leap
• arxiv: https://arxiv.org/abs/1611.10010

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

• arxiv: https://arxiv.org/abs/1612.03019

Deep Learning Logo Detection with Data Expansion by Synthesising Context

• arxiv: https://arxiv.org/abs/1612.09322

Scalable Deep Learning Logo Detection

• arxiv: https://arxiv.org/abs/1803.11417

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

• arxiv: https://arxiv.org/abs/1702.00307

Automatic Handgun Detection Alarm in Videos Using Deep Learning

• arxiv: https://arxiv.org/abs/1702.05147
• results: https://github.com/SihamTabik/Pistol-Detection-in-Videos

Objects as context for part detection

• arxiv: https://arxiv.org/abs/1703.09529

Using Deep Networks for Drone Detection

• intro: AVSS 2017
• arxiv: https://arxiv.org/abs/1706.05726

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

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1708.01642

Target Driven Instance Detection

• arxiv: https://arxiv.org/abs/1803.04610

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

• arxiv: https://arxiv.org/abs/1709.04577

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

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1710.06288
• github: https://github.com/SeokjuLee/VPGNet

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

• arxiv: https://arxiv.org/abs/1711.05128

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

• intro: WACV 2018
• arxiv: https://arxiv.org/abs/1801.02031

Deep Learning Object Detection Methods for Ecological Camera Trap Data

• intro: Conference of Computer and Robot Vision. University of Guelph
• arxiv: https://arxiv.org/abs/1803.10842

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

• arxiv: https://arxiv.org/abs/1806.05525

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

• arxiv: https://arxiv.org/abs/1802.05591
• github: https://github.com/MaybeShewill-CV/lanenet-lane-detection

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

• intro: BMVC 2018
• project page: https://gaochen315.github.io/iCAN/
• arxiv: https://arxiv.org/abs/1808.10437
• github: https://github.com/vt-vl-lab/iCAN

Densely Supervised Grasp Detector (DSGD)

• https://arxiv.org/abs/1810.03962

---

Object Proposal

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

• arxiv: http://arxiv.org/abs/1510.04445
• github: https://github.com/aghodrati/deepproposal

Scale-aware Pixel-wise Object Proposal Networks

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1601.04798

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

• intro: BMVC 2016. AttractioNet
• arxiv: https://arxiv.org/abs/1606.04446
• github: https://github.com/gidariss/AttractioNet

Learning to Segment Object Proposals via Recursive Neural Networks

• arxiv: https://arxiv.org/abs/1612.01057

Learning Detection with Diverse Proposals

• intro: CVPR 2017
• keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
• arxiv: https://arxiv.org/abs/1704.03533

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

• keywords: product detection
• arxiv: https://arxiv.org/abs/1704.06752

Improving Small Object Proposals for Company Logo Detection

• intro: ICMR 2017
• arxiv: https://arxiv.org/abs/1704.08881

Open Logo Detection Challenge

• intro: BMVC 2018
• keywords: QMUL-OpenLogo
• project page: https://qmul-openlogo.github.io/
• arxiv: https://arxiv.org/abs/1807.01964

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

• intro: ACCV 2018 oral
• arxiv: https://arxiv.org/abs/1811.08728
• github: https://github.com/chwilms/AttentionMask

---

Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

• intro: PhD Thesis
• homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
• phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
• github(“SDS using hypercolumns”): https://github.com/bharath272/sds

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

• arxiv: http://arxiv.org/abs/1503.00949

Weakly Supervised Object Localization Using Size Estimates

• arxiv: http://arxiv.org/abs/1608.04314

Active Object Localization with Deep Reinforcement Learning

• intro: ICCV 2015
• keywords: Markov Decision Process
• arxiv: https://arxiv.org/abs/1511.06015

Localizing objects using referring expressions

• intro: ECCV 2016
• keywords: LSTM, multiple instance learning (MIL)
• paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
• github: https://github.com/varun-nagaraja/referring-expressions

LocNet: Improving Localization Accuracy for Object Detection

• intro: CVPR 2016 oral
• arxiv: http://arxiv.org/abs/1511.07763
• github: https://github.com/gidariss/LocNet

Learning Deep Features for Discriminative Localization

• homepage: http://cnnlocalization.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.04150
• github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector
• github: https://github.com/metalbubble/CAM
• github: https://github.com/tdeboissiere/VGG16CAM-keras

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

• intro: ECCV 2016
• project page: http://www.di.ens.fr/willow/research/contextlocnet/
• arxiv: http://arxiv.org/abs/1609.04331
• github: https://github.com/vadimkantorov/contextlocnet

Ensemble of Part Detectors for Simultaneous Classification and Localization

• arxiv: https://arxiv.org/abs/1705.10034

STNet: Selective Tuning of Convolutional Networks for Object Localization

• arxiv: https://arxiv.org/abs/1708.06418

Soft Proposal Networks for Weakly Supervised Object Localization

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1709.01829

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

• intro: ACM MM 2017
• arxiv: https://arxiv.org/abs/1709.08295

---

Tutorials / Talks

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

• slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf

Towards Good Practices for Recognition & Detection

• intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
• slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf

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

https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit#slide=id.g37418adc7a_0_229

Object Detection with Deep Learning: A Review

• arxiv: https://arxiv.org/abs/1807.05511

---

Projects

Detectron

• intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
• github: https://github.com/facebookresearch/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

• github: https://github.com/fmassa/object-detection.torch

Using DIGITS to train an Object Detection network

• github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md

FCN-MultiBox Detector

• intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
• github: https://github.com/teaonly/FMD.torch

KittiBox: A car detection model implemented in Tensorflow.

• keywords: MultiNet
• intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
• github: https://github.com/MarvinTeichmann/KittiBox

Deformable Convolutional Networks + MST + Soft-NMS

• github: https://github.com/bharatsingh430/Deformable-ConvNets

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

• blog: https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce
• github: https://github.com//victordibia/handtracking

Metrics for object detection

• intro: Most popular metrics used to evaluate object detection algorithms
• github: https://github.com/rafaelpadilla/Object-Detection-Metrics

MobileNetv2-SSDLite

• intro: Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow.
• github: https://github.com/chuanqi305/MobileNetv2-SSDLite

---

Leaderboard

Detection Results: VOC2012

• intro: Competition “comp4” (train on additional data)
• homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

---

Tools

BeaverDam: Video annotation tool for deep learning training labels

https://github.com/antingshen/BeaverDam

---

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

Introducing automatic object detection to visual search (Pinterest)

• keywords: Faster R-CNN
• blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
• demo: https://engineering.pinterest.com/sites/engineering/files/Visual Search V1 - Video.mp4
• review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

Deep Learning for Object Detection with DIGITS

• blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

Analyzing The Papers Behind Facebook’s Computer Vision Approach

• keywords: DeepMask, SharpMask, MultiPathNet
• blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

Easily Create High Quality Object Detectors with Deep Learning

• intro: dlib v19.2
• blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

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

• blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
• github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN

Object Detection in Satellite Imagery, a Low Overhead Approach

• part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
• part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64

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

• part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
• part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t

Faster R-CNN Pedestrian and Car Detection

• blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
• ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
• github: https://github.com/bigsnarfdude/Faster-RCNN_TF

Small U-Net for vehicle detection

• blog: https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad

Region of interest pooling explained

• blog: https://deepsense.io/region-of-interest-pooling-explained/
• github: https://github.com/deepsense-io/roi-pooling

Supercharge your Computer Vision models with the TensorFlow Object Detection API

• blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
• github: https://github.com/tensorflow/models/tree/master/object_detection

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

https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab

One-shot object detection

http://machinethink.net/blog/object-detection/

An overview of object detection: one-stage methods

https://www.jeremyjordan.me/object-detection-one-stage/

deep learning object detection

• intro: A paper list of object detection using deep learning.
• github: https://github.com/hoya012/deep_learning_object_detection