This is a list of awesome articles about object detection.
- R-CNN
- Fast R-CNN
- Faster R-CNN
- Light-Head R-CNN
- Cascade R-CNN
- SPP-Net
- YOLO
- YOLOv2
- YOLOv3
- YOLT
- SSD
- DSSD
- FSSD
- ESSD
- MDSSD
- Pelee
- Fire SSD
- R-FCN
- FPN
- DSOD
- RetinaNet
- MegNet
- RefineNet
- DetNet
- SSOD
- CornerNet
- 3D Object Detection
- ZSD（Zero-Shot Object Detection）
- OSD（One-Shot object Detection）
- Softer-NMS
- 2018
- Other
Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》
-
intro: awesome
《Deep Learning for Generic Object Detection: A Survey》
- intro: Submitted to IJCV 2018
- arXiv: https://arxiv.org/abs/1809.02165
Rich feature hierarchies for accurate object detection and semantic segmentation
- intro: R-CNN
- arxiv: http://arxiv.org/abs/1311.2524
- supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
- slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
- slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
- github: https://github.com/rbgirshick/rcnn
- notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
- caffe-pr("Make R-CNN the Caffe detection example"): BVLC/caffe#482
Fast R-CNN
- arxiv: http://arxiv.org/abs/1504.08083
- slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
- github: https://github.com/rbgirshick/fast-rcnn
- github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
- webcam demo: rbgirshick/fast-rcnn#29
- notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
- notes: http://blog.csdn.net/linj_m/article/details/48930179
- github("Fast R-CNN in MXNet"): https://github.com/precedenceguo/mx-rcnn
- github: https://github.com/mahyarnajibi/fast-rcnn-torch
- github: https://github.com/apple2373/chainer-simple-fast-rnn
- github: https://github.com/zplizzi/tensorflow-fast-rcnn
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03414
- paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
- github(Caffe): https://github.com/xiaolonw/adversarial-frcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- intro: NIPS 2015
- arxiv: http://arxiv.org/abs/1506.01497
- gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
- slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
- github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
- github(Caffe): https://github.com/rbgirshick/py-faster-rcnn
- github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
- github(PyTorch--recommend): https://github.com//jwyang/faster-rcnn.pytorch
- github: https://github.com/mitmul/chainer-faster-rcnn
- github(Torch):: https://github.com/andreaskoepf/faster-rcnn.torch
- github(Torch):: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
- github(TensorFlow): https://github.com/smallcorgi/Faster-RCNN_TF
- github(TensorFlow): https://github.com/CharlesShang/TFFRCNN
- github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
- github(Keras): https://github.com/yhenon/keras-frcnn
- github: https://github.com/Eniac-Xie/faster-rcnn-resnet
- github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev
R-CNN minus R
- intro: BMVC 2015
- arxiv: http://arxiv.org/abs/1506.06981
Faster R-CNN in MXNet with distributed implementation and data parallelization
Contextual Priming and Feedback for Faster R-CNN
- intro: ECCV 2016. Carnegie Mellon University
- paper: http://abhinavsh.info/context_priming_feedback.pdf
- poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf
An Implementation of Faster RCNN with Study for Region Sampling
- intro: Technical Report, 3 pages. CMU
- arxiv: https://arxiv.org/abs/1702.02138
- github: https://github.com/endernewton/tf-faster-rcnn
- github: https://github.com/ruotianluo/pytorch-faster-rcnn
Interpretable R-CNN
- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
- arxiv: https://arxiv.org/abs/1711.05226
Domain Adaptive Faster R-CNN for Object Detection in the Wild
- intro: CVPR 2018. ETH Zurich & ESAT/PSI
- arxiv: https://arxiv.org/abs/1803.03243
Light-Head R-CNN: In Defense of Two-Stage Object Detector
- intro: Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07264
- github(offical): https://github.com/zengarden/light_head_rcnn
- github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784
Cascade R-CNN: Delving into High Quality Object Detection
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- intro: ECCV 2014 / TPAMI 2015
- arxiv: http://arxiv.org/abs/1406.4729
- github: https://github.com/ShaoqingRen/SPP_net
- notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
- intro: PAMI 2016
- intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
- project page: http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
- arxiv: http://arxiv.org/abs/1412.5661
Object Detectors Emerge in Deep Scene CNNs
- intro: ICLR 2015
- arxiv: http://arxiv.org/abs/1412.6856
- paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
- paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
- slides: http://places.csail.mit.edu/slide_iclr2015.pdf
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
- intro: CVPR 2015
- project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
- arxiv: https://arxiv.org/abs/1502.04275
- github: https://github.com/YknZhu/segDeepM
Object Detection Networks on Convolutional Feature Maps
- intro: TPAMI 2015
- keywords: NoC
- arxiv: http://arxiv.org/abs/1504.06066
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
- arxiv: http://arxiv.org/abs/1504.03293
- slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
- github: https://github.com/YutingZhang/fgs-obj
DeepBox: Learning Objectness with Convolutional Networks
- keywords: DeepBox
- arxiv: http://arxiv.org/abs/1505.02146
- github: https://github.com/weichengkuo/DeepBox
You Only Look Once: Unified, Real-Time Object Detection
- arxiv: http://arxiv.org/abs/1506.02640
- code: https://pjreddie.com/darknet/yolov1/
- github: https://github.com/pjreddie/darknet
- blog: https://pjreddie.com/darknet/yolov1/
- slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
- reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
- github: https://github.com/gliese581gg/YOLO_tensorflow
- github: https://github.com/xingwangsfu/caffe-yolo
- github: https://github.com/frankzhangrui/Darknet-Yolo
- github: https://github.com/BriSkyHekun/py-darknet-yolo
- github: https://github.com/tommy-qichang/yolo.torch
- github: https://github.com/frischzenger/yolo-windows
- github: https://github.com/AlexeyAB/yolo-windows
- github: https://github.com/nilboy/tensorflow-yolo
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
- blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
- github: https://github.com/thtrieu/darkflow
Start Training YOLO with Our Own Data
- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: http://guanghan.info/blog/en/my-works/train-yolo/
- github: https://github.com/Guanghan/darknet
YOLO: Core ML versus MPSNNGraph
- intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
- github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph
TensorFlow YOLO object detection on Android
- intro: Real-time object detection on Android using the YOLO network with TensorFlow
- github: https://github.com/natanielruiz/android-yolo
Computer Vision in iOS – Object Detection
- blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
- github:https://github.com/r4ghu/iOS-CoreML-Yolo
YOLO9000: Better, Faster, Stronger
- arxiv: https://arxiv.org/abs/1612.08242
- code: http://pjreddie.com/yolo9000/ https://pjreddie.com/darknet/yolov2/
- github(Chainer): https://github.com/leetenki/YOLOv2
- github(Keras): https://github.com/allanzelener/YAD2K
- github(PyTorch): https://github.com/longcw/yolo2-pytorch
- github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
- github(Windows): https://github.com/AlexeyAB/darknet
- github: https://github.com/choasUp/caffe-yolo9000
- github: https://github.com/philipperemy/yolo-9000
- github(TensorFlow): https://github.com/KOD-Chen/YOLOv2-Tensorflow
- github(Keras): https://github.com/yhcc/yolo2
- github(Keras): https://github.com/experiencor/keras-yolo2
- github(TensorFlow): https://github.com/WojciechMormul/yolo2
darknet_scripts
- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
- github: https://github.com/Jumabek/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
https://github.com//explosion/lightnet
YOLO v2 Bounding Box Tool
- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
- github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
- arxiv: https://arxiv.org/abs/1804.04606
Object detection at 200 Frames Per Second
- intro: faster than Tiny-Yolo-v2
- arxiv: https://arxiv.org/abs/1805.06361
Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras
- intro: YOLE--Object Detection in Neuromorphic Cameras
- arxiv:https://arxiv.org/abs/1805.07931
OmniDetector: With Neural Networks to Bounding Boxes
- intro: a person detector on n fish-eye images of indoor scenes（NIPS 2018）
- arxiv:https://arxiv.org/abs/1805.08503
- datasets:https://gitlab.com/omnidetector/omnidetector
YOLOv3: An Incremental Improvement
- arxiv:https://arxiv.org/abs/1804.02767
- paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf
- code: https://pjreddie.com/darknet/yolo/
- github(Official):https://github.com/pjreddie/darknet
- github:https://github.com/experiencor/keras-yolo3
- github:https://github.com/qqwweee/keras-yolo3
- github:https://github.com/marvis/pytorch-yolo3
- github:https://github.com/ayooshkathuria/pytorch-yolo-v3
- github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch
- github:https://github.com/eriklindernoren/PyTorch-YOLOv3
You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
-
intro: Small Object Detection
- intro: ECCV 2016 Oral
- arxiv: http://arxiv.org/abs/1512.02325
- paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
- slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf
- github(Official): https://github.com/weiliu89/caffe/tree/ssd
- video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
- github: https://github.com/zhreshold/mxnet-ssd
- github: https://github.com/zhreshold/mxnet-ssd.cpp
- github: https://github.com/rykov8/ssd_keras
- github: https://github.com/balancap/SSD-Tensorflow
- github: https://github.com/amdegroot/ssd.pytorch
- github(Caffe): https://github.com/chuanqi305/MobileNet-SSD
- intro: UNC Chapel Hill & Amazon Inc
- arxiv: https://arxiv.org/abs/1701.06659
- github: https://github.com/chengyangfu/caffe/tree/dssd
- github: https://github.com/MTCloudVision/mxnet-dssd
- demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4
- intro: rainbow SSD (R-SSD)
- arxiv: https://arxiv.org/abs/1705.09587
- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
- arxiv: https://arxiv.org/abs/1707.08682
- intro: WeaveNet
- keywords: fuse multi-scale information
- arxiv: https://arxiv.org/abs/1712.03149
-
intro: (ICLR 2018 workshop track)
-
intro:low cost, fast speed and high mAP on factor edge computing devices
- arxiv: http://arxiv.org/abs/1605.06409
- github: https://github.com/daijifeng001/R-FCN
- github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
- github: https://github.com/Orpine/py-R-FCN
- github: https://github.com/PureDiors/pytorch_RFCN
- github: https://github.com/bharatsingh430/py-R-FCN-multiGPU
- github: https://github.com/xdever/RFCN-tensorflow
- intro: Facebook AI Research
- arxiv: https://arxiv.org/abs/1612.03144
- intro: CMU & UC Berkeley & Google Research
- arxiv: https://arxiv.org/abs/1612.06851
- intro: Inha University
- arxiv: https://arxiv.org/abs/1702.01243
- intro: University of Maryland & Mitsubishi Electric Research Laboratories
- arxiv: https://arxiv.org/abs/1702.01478
- keykwords: CC-Net
- intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
- arxiv: https://arxiv.org/abs/1702.07054
- intro: ICCV 2017 (poster)
- arxiv: https://arxiv.org/abs/1703.10295
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03944
- intro: CVPR 2017. SenseTime
- keywords: Recurrent Rolling Convolution (RRC)
- arxiv: https://arxiv.org/abs/1704.05776
- github: https://github.com/xiaohaoChen/rrc_detection
- intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
- arxiv: https://arxiv.org/abs/1705.05922
- intro: Point Linking Network (PLN)
- arxiv: https://arxiv.org/abs/1706.03646
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1707.01691
- github: https://github.com/taokong/RON
- intro: CVPR 2017. SenseTime & Beihang University
- paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf
- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
- arxiv: https://arxiv.org/abs/1707.06175
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1707.06399
- intro: ICCV 2017
- keywords: Recurrent Scale Approximation (RSA)
- arxiv: https://arxiv.org/abs/1707.09531
- github: https://github.com/sciencefans/RSA-for-object-detection
- intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
- arxiv: https://arxiv.org/abs/1708.01241
- github: https://github.com/szq0214/DSOD
- github:https://github.com/Windaway/DSOD-Tensorflow
- github:https://github.com/chenyuntc/dsod.pytorch
- intro: BMVC 2018
- arXiv: https://arxiv.org/abs/1807.11013
- intro: This is an extended version of DSOD
- arXiv: https://arxiv.org/abs/1809.09294
- intro: ICCV 2017 Best student paper award. Facebook AI Research
- keywords: RetinaNet
- arxiv: https://arxiv.org/abs/1708.02002
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.02863
- intro: ICCV 2017. Inria
- arxiv: https://arxiv.org/abs/1708.06977
- intro: NTU, Singapore & Amazon
- keywords: multi-instance multi-label domain adaption learning framework
- arxiv: https://arxiv.org/abs/1711.05954
- intro: Peking University & Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07240
- intro: RFBNet
- arxiv: https://arxiv.org/abs/1711.07767
- github: https://github.com//ruinmessi/RFBNet
- intro: Microsoft AI & Research Munich
- arxiv: https://arxiv.org/abs/1711.09822
- keywords: region selection network, gating network
- arxiv: https://arxiv.org/abs/1712.02408
- intro: IEEE/CAA Journal of Automatica Sinica
- arxiv: https://arxiv.org/abs/1712.08470
- keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
- arxiv: https://arxiv.org/abs/1712.08832
- intro: Tsinghua University & JD Group
- arxiv: https://arxiv.org/abs/1801.01051
- intro: AAAI 2018
- arxiv: https://arxiv.org/abs/1803.01529
- intro: Peking University & MSRA
- arxiv: https://arxiv.org/abs/1803.07066
- intro: Singapore Management University & Zhejiang University
- arxiv: https://arxiv.org/abs/1803.08208
- intro: University of Tokyo & National Institute of Informatics, Japan
- arxiv: https://arxiv.org/abs/1803.08670
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1804.00428
- github: https://github.com/Hwang64/MLKP
- intro: National University of Defense Technology
- arxiv: https://arxiv.org/abs/1804.04606
-
intro: CVPR 2018
- intro: Tsinghua University & Face++
- arxiv: https://arxiv.org/abs/1804.06215
- Google Brain
- arxiv:https://arxiv.org/abs/1806.03370
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.01244
- github: https://github.com/umich-vl/CornerNet
- arxiv: https://arxiv.org/abs/1805.04902
- github: https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection
- intro: Australian National University
- keywords: YOLO
- arxiv: https://arxiv.org/abs/1803.07113
- intro: IBM Research AI
- arxiv:https://arxiv.org/abs/1806.04728
- github: TODO
- intro: CMU & Face++
- arXiv: https://arxiv.org/abs/1809.08545
- github: https://github.com/yihui-he/softer-NMS
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1711.07767
- github: https://github.com/ruinmessi/RFBNet
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.07993
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1808.04285
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1807.11590
- github: https://github.com/vacancy/PreciseRoIPooling
- intro: ECCV 2018
- arXiv: https://arxiv.org/abs/1807.09528
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1711.11575
- github:https://github.com/msracver/Relation-Networks-for-Object-Detection
- Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
- arxiv: https://arxiv.org/abs/1805.02152
- intro: CVPR 2018 Camera Ready
- arxiv: https://arxiv.org/abs/1805.04953
- intro: the robustness of object detection under the presence of missing annotations
- arxiv:https://arxiv.org/abs/1806.06986
- intro: TNNLS 2018
- arxiv:https://arxiv.org/abs/1807.00147
- code: http://kezewang.com/codes/ASM_ver1.zip
- arxiv: https://arxiv.org/abs/1808.05560
- youtube: https://youtu.be/xCYD-tYudN0
SSD: Single Shot MultiBox Detector
What's the diffience in performance between this new code you pushed and the previous code? #327
DSSD : Deconvolutional Single Shot Detector
Enhancement of SSD by concatenating feature maps for object detection
Context-aware Single-Shot Detector
Feature-Fused SSD: Fast Detection for Small Objects
https://arxiv.org/abs/1709.05054
FSSD: Feature Fusion Single Shot Multibox Detector
https://arxiv.org/abs/1712.00960
Weaving Multi-scale Context for Single Shot Detector
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
https://arxiv.org/abs/1801.05918
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
https://arxiv.org/abs/1802.06488
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Robert-JunWang/Pelee
Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN-3000 at 30fps: Decoupling Detection and Classification
https://arxiv.org/abs/1712.01802
Recycle deep features for better object detection
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
https://arxiv.org/abs/1704.05775
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
https://arxiv.org/abs/1706.05274
Few-shot Object Detection
https://arxiv.org/abs/1706.08249
Yes-Net: An effective Detector Based on Global Information
https://arxiv.org/abs/1706.09180
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
https://arxiv.org/abs/1706.10217
Towards lightweight convolutional neural networks for object detection
https://arxiv.org/abs/1707.01395
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
https://arxiv.org/abs/1707.05031
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: Learning Deeply Supervised Object Detectors from Scratch
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Object Detection from Scratch with Deep Supervision
Focal Loss for Dense Object Detection
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
https://arxiv.org/abs/1709.04347
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
https://arxiv.org/abs/1709.05788
Dynamic Zoom-in Network for Fast Object Detection in Large Images
https://arxiv.org/abs/1711.05187
Zero-Annotation Object Detection with Web Knowledge Transfer
MegDet: A Large Mini-Batch Object Detector
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
https://arxiv.org/abs/1711.08879
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
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
Pseudo Mask Augmented Object Detection
https://arxiv.org/abs/1803.05858
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
https://arxiv.org/abs/1803.06799
Learni 8000 ng 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
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Single-Shot Refinement Neural Network for Object Detection
DetNet: A Backbone network for Object Detection
Self-supervisory Signals for Object Discovery and Detection
CornerNet: Detecting Objects as Paired Keypoints
LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs
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
One-Shot Object Detection
RepMet: Representative-based metric learning for classification and one-shot object detection
《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》
《Receptive Field Block Net for Accurate and Fast Object Detection》
Deep Feature Pyramid Reconfiguration for Object Detection
Unsupervised Hard Example Mining from Videos for Improved Object Detection
Acquisition of Localization Confidence for Accurate Object Detection
Toward Scale-Invariance and Position-Sensitive Region Proposal Networks
MetaAnchor: Learning to Detect Objects with Customized Anchors
Relation Network for Object Detection
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Learning Rich Features for Image Manipulation Detection
SNIPER: Efficient Multi-Scale Training
Soft Sampling for Robust Object Detection
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos