Recent Advances in Deep Learning for Object Detection

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning. Keywords: Object Detection, Deep Learning, Deep Convolutional Neural Networks

[1]  Sami Romdhani,et al.  Efficient Face Detection by a Cascaded Support Vector Machine Using Haar-Like Features , 2004, DAGM-Symposium.

[2]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[3]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  Song Han,et al.  Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.

[5]  Bingbing Ni,et al.  Scale-Transferrable Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[6]  Abhinav Gupta,et al.  Contextual Priming and Feedback for Faster R-CNN , 2016, ECCV.

[7]  Ronan Collobert,et al.  Learning to Refine Object Segments , 2016, ECCV.

[8]  Sanja Fidler,et al.  segDeepM: Exploiting segmentation and context in deep neural networks for object detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Yu Liu,et al.  Zoom Out-and-In Network with Recursive Training for Object Proposal , 2017, ArXiv.

[10]  Ran He,et al.  PyramidBox++: High Performance Detector for Finding Tiny Face , 2019, ArXiv.

[11]  Alexander Wong,et al.  Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection , 2018, 2018 15th Conference on Computer and Robot Vision (CRV).

[12]  Hanqing Lu,et al.  CoupleNet: Coupling Global Structure with Local Parts for Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  Sergio Guadarrama,et al.  Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Joon Hee Han,et al.  Local Decorrelation For Improved Detection , 2014, ArXiv.

[15]  Shifeng Zhang,et al.  Improved Selective Refinement Network for Face Detection , 2019, ArXiv.

[16]  Shu Liu,et al.  Path Aggregation Network for Instance Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Chunluan Zhou,et al.  Bi-box Regression for Pedestrian Detection and Occlusion Estimation , 2018, ECCV.

[18]  Yuning Jiang,et al.  Repulsion Loss: Detecting Pedestrians in a Crowd , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Honglak Lee,et al.  Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units , 2016, ICML.

[20]  Xiaoming Liu,et al.  Illuminating Pedestrians via Simultaneous Detection and Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[21]  Fuchun Sun,et al.  Deep Feature Pyramid Reconfiguration for Object Detection , 2018, ECCV.

[22]  Derek Hoiem,et al.  Category-Independent Object Proposals with Diverse Ranking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Shaogang Gong,et al.  Scalable Deep Learning Logo Detection , 2018, ArXiv.

[24]  Hao Wang,et al.  Detecting Faces Using Inside Cascaded Contextual CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[25]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[26]  Stan Z. Li,et al.  Single-Shot Scale-Aware Network for Real-Time Face Detection , 2019, International Journal of Computer Vision.

[27]  Yuning Jiang,et al.  What Can Help Pedestrian Detection? , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Luc Van Gool,et al.  Handling Occlusions with Franken-Classifiers , 2013, 2013 IEEE International Conference on Computer Vision.

[29]  Steven L. Waslander,et al.  A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection , 2018, 2018 15th Conference on Computer and Robot Vision (CRV).

[30]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Tara Javidi,et al.  Adaptive Object Detection Using Adjacency and Zoom Prediction , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[33]  Kaiming He,et al.  Rethinking ImageNet Pre-Training , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[34]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[35]  Tong Yang,et al.  MetaAnchor: Learning to Detect Objects with Customized Anchors , 2018, NeurIPS.

[36]  Rogério Schmidt Feris,et al.  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.

[37]  Bhiksha Raj,et al.  SphereFace: Deep Hypersphere Embedding for Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[39]  Wei Liu,et al.  DSSD : Deconvolutional Single Shot Detector , 2017, ArXiv.

[40]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[41]  Bin Yang,et al.  Aggregate channel features for multi-view face detection , 2014, IEEE International Joint Conference on Biometrics.

[42]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[43]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[44]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Fahad Shahbaz Khan,et al.  Mask-Guided Attention Network for Occluded Pedestrian Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[46]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[47]  Fuchun Sun,et al.  HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Eunhyeok Park,et al.  Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications , 2015, ICLR.

[49]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[50]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  Houqiang Li,et al.  Feature Selective Networks for Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[52]  Zhaoxiang Zhang,et al.  Scale-Aware Trident Networks for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[53]  Ming Tang,et al.  PCN: Part and Context Information for Pedestrian Detection with CNNs , 2018, BMVC.

[54]  Bo Wang,et al.  Single-Shot Object Detection with Enriched Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[55]  Baoli Li,et al.  Traffic-Sign Detection and Classification in the Wild , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Liang Lin,et al.  Is Faster R-CNN Doing Well for Pedestrian Detection? , 2016, ECCV.

[57]  Jonathan Krause,et al.  Fine-Grained Car Detection for Visual Census Estimation , 2017, AAAI.

[58]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[59]  Shuo Yang,et al.  Face Detection through Scale-Friendly Deep Convolutional Networks , 2017, ArXiv.

[60]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[61]  Xiaogang Wang,et al.  DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[63]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[64]  Yichen Wei,et al.  Learning Region Features for Object Detection , 2018, ECCV.

[65]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[66]  Frédéric Jurie,et al.  Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks , 2018, ArXiv.

[67]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[68]  Abhinav Gupta,et al.  Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[69]  Jinjun Xiong,et al.  Revisiting RCNN: On Awakening the Classification Power of Faster RCNN , 2018, ECCV.

[70]  Matthew B. Blaschko,et al.  Learning a category independent object detection cascade , 2011, 2011 International Conference on Computer Vision.

[71]  Bo Li,et al.  Auto-Context R-CNN , 2018, ArXiv.

[72]  Lianwen Jin,et al.  DeRPN: Taking a Further Step toward More General Object Detection , 2018, AAAI.

[73]  Shuicheng Yan,et al.  Scale-Aware Fast R-CNN for Pedestrian Detection , 2015, IEEE Transactions on Multimedia.

[74]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[75]  Hao Chen,et al.  LSTD: A Low-Shot Transfer Detector for Object Detection , 2018, AAAI.

[76]  Yunhong Wang,et al.  Receptive Field Block Net for Accurate and Fast Object Detection , 2017, ECCV.

[77]  Yuning Jiang,et al.  MegDet: A Large Mini-Batch Object Detector , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[78]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[79]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[80]  Stefanos Zafeiriou,et al.  RetinaFace: Single-stage Dense Face Localisation in the Wild , 2019, ArXiv.

[81]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[82]  Marios Savvides,et al.  CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection , 2016, ArXiv.

[83]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[84]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[85]  William J. Dally,et al.  Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training , 2017, ICLR.

[86]  Weiyao Lin,et al.  Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages , 2018, BMVC.

[87]  Serge J. Belongie,et al.  Context based object categorization: A critical survey , 2010, Comput. Vis. Image Underst..

[88]  Jian Cheng,et al.  Quantized Convolutional Neural Networks for Mobile Devices , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[89]  Raquel Urtasun,et al.  Understanding the Effective Receptive Field in Deep Convolutional Neural Networks , 2016, NIPS.

[90]  Jaeseok Choi,et al.  Residual Features and Unified Prediction Network for Single Stage Detection , 2017, ArXiv.

[91]  Fuqiang Zhou,et al.  FSSD: Feature Fusion Single Shot Multibox Detector , 2017, ArXiv.

[92]  Bernt Schiele,et al.  Learning Non-maximum Suppression , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[93]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[94]  Yu-Wing Tai,et al.  Accurate Single Stage Detector Using Recurrent Rolling Convolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[95]  Junjie Yan,et al.  Mimicking Very Efficient Network for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[96]  Hao Wang,et al.  Face R-CNN , 2017, ArXiv.

[97]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[98]  Xiaogang Wang,et al.  Object Detection from Video Tubelets with Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[99]  Wei Liu,et al.  High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[100]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[101]  Seyed Majid Azimi,et al.  ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery , 2018, ECCV Workshops.

[102]  Fan Yang,et al.  Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[103]  Yu Liu,et al.  Recurrent Scale Approximation for Object Detection in CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[104]  Junjie Yan,et al.  Grid R-CNN , 2018, 1811.12030.

[105]  Kunihiko Fukushima,et al.  Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition , 1982 .

[106]  Larry S. Davis,et al.  Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[107]  Jitendra Malik,et al.  Deformable part models are convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[108]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[109]  Jian Yang,et al.  DSFD: Dual Shot Face Detector , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[110]  Ross Girshick,et al.  Discriminatively Trained Mixtures of Deformable Part Models , 2008 .

[111]  Ying Chen,et al.  M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network , 2018, AAAI.

[112]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[113]  Sharath Pankanti,et al.  RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[114]  Shifeng Zhang,et al.  S^3FD: Single Shot Scale-Invariant Face Detector , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[115]  Nikos Komodakis,et al.  LocNet: Improving Localization Accuracy for Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[116]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[117]  Shuicheng Yan,et al.  Seq-NMS for Video Object Detection , 2016, ArXiv.

[118]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[119]  Yann LeCun,et al.  Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[120]  Shuicheng Yan,et al.  Dual Path Networks , 2017, NIPS.

[121]  Paramartha Dutta,et al.  A Review of Object Detection Models based on Convolutional Neural Network , 2019, Advances in Intelligent Systems and Computing.

[122]  Haifeng Shen,et al.  Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision , 2018, ArXiv.

[123]  Yeongjae Cheon,et al.  PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection , 2016, ArXiv.

[124]  Qi Tian,et al.  CenterNet: Keypoint Triplets for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[125]  Xiaowei Zhou,et al.  Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[126]  Xingyi Zhou,et al.  Bottom-Up Object Detection by Grouping Extreme and Center Points , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[127]  Bernt Schiele,et al.  CityPersons: A Diverse Dataset for Pedestrian Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[128]  Xiangyu Zhang,et al.  DetNAS: Neural Architecture Search on Object Detection , 2019, ArXiv.

[129]  Ran Tao,et al.  Seeing Small Faces from Robust Anchor's Perspective , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[130]  Huimin Ma,et al.  Semantic Head Enhanced Pedestrian Detection in a Crowd , 2019, Neurocomputing.

[131]  Shiguang Shan,et al.  Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[132]  Mohan M. Trivedi,et al.  To boost or not to boost? On the limits of boosted trees for object detection , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[133]  Xingyi Zhou,et al.  Objects as Points , 2019, ArXiv.

[134]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[135]  Rama Chellappa,et al.  Deep Regionlets for Object Detection , 2017, ECCV.

[136]  Shifeng Zhang,et al.  Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd , 2018, ECCV.

[137]  Andrew Blake,et al.  Computationally efficient face detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[138]  Sanja Fidler,et al.  Bottom-Up Segmentation for Top-Down Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[139]  Xiangyu Zhang,et al.  Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[140]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[141]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[142]  Nuno Vasconcelos,et al.  Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[143]  Yan Wang,et al.  Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[144]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[145]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[146]  Larry S. Davis,et al.  SNIPER: Efficient Multi-Scale Training , 2018, NeurIPS.

[147]  Hao Wang,et al.  Multi-scale Location-Aware Kernel Representation for Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[148]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[149]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[150]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[151]  Cordelia Schmid,et al.  Incremental Learning of Object Detectors without Catastrophic Forgetting , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[152]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[153]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[154]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[155]  Shuicheng Yan,et al.  Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids , 2017, ArXiv.

[156]  Steven C. H. Hoi,et al.  Feature Agglomeration Networks for Single Stage Face Detection , 2017, Neurocomputing.

[157]  Ming Yang,et al.  Compressing Deep Convolutional Networks using Vector Quantization , 2014, ArXiv.

[158]  Cristian Sminchisescu,et al.  CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[159]  Lars Petersson,et al.  DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[160]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[161]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[162]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[163]  Santiago Manen,et al.  Prime Object Proposals with Randomized Prim's Algorithm , 2013, 2013 IEEE International Conference on Computer Vision.

[164]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[165]  Bernt Schiele,et al.  Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.

[166]  Shuo Yang,et al.  WIDER FACE: A Face Detection Benchmark , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[167]  Zhe Chen,et al.  Context Refinement for Object Detection , 2018, ECCV.

[168]  Lars Petersson,et al.  Improving Object Localization with Fitness NMS and Bounded IoU Loss , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[169]  Xiangyu Zhang,et al.  Light-Head R-CNN: In Defense of Two-Stage Object Detector , 2017, ArXiv.

[170]  Xiaogang Wang,et al.  DeepID3: Face Recognition with Very Deep Neural Networks , 2015, ArXiv.

[171]  Marios Savvides,et al.  Feature Selective Anchor-Free Module for Single-Shot Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[172]  Xiaogang Wang,et al.  Deep Learning Strong Parts for Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[173]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[174]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[175]  Yu Liu,et al.  Gradient Harmonized Single-stage Detector , 2018, AAAI.

[176]  Soumith Chintala,et al.  A MultiPath Network for Object Detection , 2016, BMVC.

[177]  Stephen Lin,et al.  Deformable ConvNets V2: More Deformable, Better Results , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[178]  Abhinav Gupta,et al.  A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[179]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[180]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[181]  Hossein Mobahi,et al.  Deep learning from temporal coherence in video , 2009, ICML '09.

[182]  Jian Sun,et al.  DetNAS: Backbone Search for Object Detection , 2019, NeurIPS.

[183]  Fuchun Sun,et al.  RON: Reverse Connection with Objectness Prior Networks for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[184]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[185]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[186]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[187]  Hao Wang,et al.  Detecting Faces Using Region-based Fully Convolutional Networks , 2017 .

[188]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[189]  Nuno Vasconcelos,et al.  Learning Complexity-Aware Cascades for Deep Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[190]  Shifeng Zhang,et al.  Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[191]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[192]  Martin D. Levine,et al.  Multi-path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces" , 2017, 2017 14th Conference on Computer and Robot Vision (CRV).

[193]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[194]  Yuning Jiang,et al.  Acquisition of Localization Confidence for Accurate Object Detection , 2018, ECCV.

[195]  Shihong Lao,et al.  A Structural Filter Approach to Human Detection , 2010, ECCV.

[196]  Jian Sun,et al.  Instance-Aware Semantic Segmentation via Multi-task Network Cascades , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[197]  Yunchao Wei,et al.  Perceptual Generative Adversarial Networks for Small Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[198]  Lisha Cui,et al.  MDSSD: multi-scale deconvolutional single shot detector for small objects , 2018, Science China Information Sciences.

[199]  Larry S. Davis,et al.  Face-MagNet: Magnifying Feature Maps to Detect Small Faces , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[201]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, International Journal of Computer Vision.

[202]  Zhiqiang Shen,et al.  DSOD: Learning Deeply Supervised Object Detectors from Scratch , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[203]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[204]  Shifeng Zhang,et al.  ScratchDet: Exploring to Train Single-Shot Object Detectors from Scratch , 2018, ArXiv.

[205]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[206]  Larry S. Davis,et al.  Bilattice-based Logical Reasoning for Human Detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[207]  Nojun Kwak,et al.  Enhancement of SSD by concatenating feature maps for object detection , 2017, BMVC.

[208]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[209]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[210]  Larry S. Davis,et al.  An Analysis of Scale Invariance in Object Detection - SNIP , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[211]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[212]  Gang Yu,et al.  Face Attention Network: An Effective Face Detector for the Occluded Faces , 2017, ArXiv.

[213]  Shuo Yang,et al.  From Facial Parts Responses to Face Detection: A Deep Learning Approach , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[214]  Nikos Komodakis,et al.  Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[215]  Xiaogang Wang,et al.  A discriminative deep model for pedestrian detection with occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[216]  Shifeng Zhang,et al.  Selective Refinement Network for High Performance Face Detection , 2018, AAAI.

[217]  Zhiqiang Shen,et al.  Improving Object Detection from Scratch via Gated Feature Reuse , 2017, BMVC.

[218]  In-So Kweon,et al.  StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[219]  Yongchao Gong,et al.  Mask Scoring R-CNN , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[220]  Xiang Xu,et al.  Face Detection Using Improved Faster RCNN , 2018, ArXiv.

[221]  Deng Cai,et al.  Deep feature based contextual model for object detection , 2016, Neurocomputing.

[222]  Bernt Schiele,et al.  Taking a deeper look at pedestrians , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[223]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[224]  Quoc V. Le,et al.  NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[225]  Xinlei Chen,et al.  Spatial Memory for Context Reasoning in Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[226]  Jiahong Wu,et al.  Accurate Face Detection for High Performance , 2019, ArXiv.

[227]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[228]  Larry S. Davis,et al.  SSH: Single Stage Headless Face Detector , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[229]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[230]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[231]  Xiaolin Hu,et al.  Scale-Aware Face Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[232]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[233]  Jitendra Malik,et al.  Beyond Skip Connections: Top-Down Modulation for Object Detection , 2016, ArXiv.

[234]  Xiaogang Wang,et al.  Single-Pedestrian Detection Aided by Multi-pedestrian Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[235]  Steven C. H. Hoi,et al.  Face Detection using Deep Learning: An Improved Faster RCNN Approach , 2017, Neurocomputing.

[236]  Bin Yang,et al.  CRAFT Objects from Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[237]  Shifeng Zhang,et al.  RefineFace: Refinement Neural Network for High Performance Face Detection , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[238]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[239]  Nicu Sebe,et al.  Learning Cross-Modal Deep Representations for Robust Pedestrian Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[240]  Qiang Wu,et al.  LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks , 2015, ArXiv.

[241]  Jungwon Lee,et al.  Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[242]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[243]  Cordelia Schmid,et al.  Combining efficient object localization and image classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[244]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[245]  Ronan Collobert,et al.  Learning to Segment Object Candidates , 2015, NIPS.

[246]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[247]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[248]  Xu Tang,et al.  PyramidBox: A Context-assisted Single Shot Face Detector , 2018, ECCV.

[249]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[250]  Ling Shao,et al.  DAVE: A Unified Framework for Fast Vehicle Detection and Annotation , 2016, ECCV.

[251]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[252]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[253]  Ross B. Girshick,et al.  LVIS: A Dataset for Large Vocabulary Instance Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[254]  Xing Xie,et al.  Spatial pyramid mining for logo detection in natural scenes , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[255]  Jian Dong,et al.  Attentive Contexts for Object Detection , 2016, IEEE Transactions on Multimedia.

[256]  Peiyun Hu,et al.  Finding Tiny Faces , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[257]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[258]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[259]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[260]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[261]  Shaogang Gong,et al.  Deep Learning Logo Detection with Data Expansion by Synthesising Context , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[262]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[263]  Jordi Pont-Tuset,et al.  The Open Images Dataset V4 , 2018, International Journal of Computer Vision.

[264]  Xiaogang Wang,et al.  Modeling Mutual Visibility Relationship in Pedestrian Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[265]  Luc Van Gool,et al.  DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[266]  Deyu Meng,et al.  Few-Example Object Detection with Model Communication , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[267]  Yichen Wei,et al.  Pseudo Mask Augmented Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[268]  Bernt Schiele,et al.  Detection and Tracking of Occluded People , 2014, International Journal of Computer Vision.

[269]  Xiangyu Zhang,et al.  DetNet: A Backbone network for Object Detection , 2018, ArXiv.

[270]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[271]  Yichen Wei,et al.  Relation Networks for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[272]  Xiaogang Wang,et al.  Gated Bi-directional CNN for Object Detection , 2016, ECCV.

[273]  Xiang Xu,et al.  Robust and High Performance Face Detector , 2019, ArXiv.

[274]  James J. Little,et al.  Exploiting Temporal Information for 3D Human Pose Estimation , 2017, ECCV.

[275]  Anelia Angelova,et al.  Real-Time Pedestrian Detection with Deep Network Cascades , 2015, BMVC.

[276]  Dariu Gavrila,et al.  Multi-cue pedestrian classification with partial occlusion handling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[277]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[278]  Dacheng Tao,et al.  Anchor Cascade for Efficient Face Detection , 2018, IEEE Transactions on Image Processing.

[279]  Guoying Zhao,et al.  SRN: Side-Output Residual Network for Object Symmetry Detection in the Wild , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[280]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[281]  Yang Li,et al.  Attribute-aware Pedestrian Detection in a Crowd , 2019 .

[282]  Quoc V. Le,et al.  Learning Data Augmentation Strategies for Object Detection , 2019, ECCV.