目标检测-20种常用深度学习算法论文、复现代码汇总

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目录

· R-CNN

· Fast R-CNN

· Faster R-CNN

· Light-Head R-CNN

· Cascade R-CNN

· SPP-Net

· YOLO

· YOLOv2

· YOLOv3

· SSD

· DSSD

· FSSD

· ESSD

· Pelee

· R-FCN

· FPN

· RetinaNet

· MegDet

· DetNet

· ZSD

R-CNN

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"): https://github.com/BVLC/caffe/pull/482

Fast R-CNN

 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: https://github.com/rbgirshick/fast-rcnn/pull/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

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(PyTorch):: https://github.com/andreaskoepf/faster-rcnn.torch

· github(PyTorch):: 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

访问AI图谱 技术分享社区 

https://loveai.tech

基于MXNet，Faster R-CNN的数据并行化的分布式实现

· github: https://github.com/dmlc/mxnet/tree/master/example/rcnn

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

关于Region Sampling的Faster RCNN实现

· intro: Technical Report, 3 pages. CMU

· arxiv: https://arxiv.org/abs/1702.02138

· github: https://github.com/endernewton/tf-faster-rcnn

可解释（Interpretable）R-CNN

· intro: North Carolina State University & Alibaba

· keywords: AND-OR Graph (AOG)

· arxiv: https://arxiv.org/abs/1711.05226

Light-Head R-CNN

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

Cascade R-CNN: Delving into High Quality Object Detection

· arxiv: https://arxiv.org/abs/1712.00726

· github: https://github.com/zhaoweicai/cascade-rcnn

SPP-Net

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

YOLO

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. 加载轻量级的模型，并基于Tensorflow对权重进行fine-tune，最终输出C++的constant graph。

· blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp

· github: https://github.com/thtrieu/darkflow

基于自己的数据Training YOLO

· 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

YOLOv2

YOLO9000: 更好，更快，更强

· 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

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

· github: https://github.com/AlexeyAB/Yolo_mark

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

· 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

YOLOv3

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

SSD

SSD: Single Shot MultiBox Detector

· 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

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

https://github.com/weiliu89/caffe/issues/327

DSSD

DSSD : Deconvolutional Single Shot Detector

· 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/http://www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4

Enhancement of SSD by concatenating feature maps for object detection

· intro: rainbow SSD (R-SSD)

· arxiv: https://arxiv.org/abs/1705.09587

Context-aware Single-Shot Detector

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

· arxiv: https://arxiv.org/abs/1707.08682

Feature-Fused SSD: Fast Detection for Small Objects

https://arxiv.org/abs/1709.05054

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

· intro: WeaveNet

· keywords: fuse multi-scale information

· arxiv: https:// arxiv.org/abs/1712.03149

ESSD

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

Pelee

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

https://github.com/Robert-JunWang/Pelee

intro: (ICLR 2018 workshop track)

arxiv: https://arxiv.org/abs/1804.06882

github: https://github.com/Robert-JunWang/Pelee

R-FCN

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

· 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

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

https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

· arxiv: http://arxiv.org/abs/1607.05066

FPN

Feature Pyramid Networks for Object Detection

· intro: Facebook AI Research

· arxiv: https://arxiv.org/abs/1612.03144

Action-Driven Object Detection with Top-Down Visual Attentions

· arxiv: https://arxiv.org/abs/1612.06704

Beyond Skip Connections: Top-Down Modulation for Object Detection

· intro: CMU & UC Berkeley & Google Research

· arxiv: https://arxiv.org/abs/1612.06851

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

· intro: Inha University

· arxiv: https://arxiv.org/abs/1702.01243

Attentional Network for Visual Object Detection

· intro: University of Maryland & Mitsubishi Electric Research Laboratories

· arxiv: https://arxiv.org/abs/1702.01478

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

· keykwords: CC-Net

· intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007

· arxiv: https://arxiv.org/abs/1702.07054

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

· intro: ICCV 2017 (poster)

· arxiv: https://arxiv.org/abs/1703.10295

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

· intro: CVPR 2017

· arxiv: https://arxiv.org/abs/1704.03944

Spatial Memory for Context Reasoning in Object Detection

· arxiv: https://arxiv.org/abs/1704.04224

Accurate Single Stage Detector Using Recurrent Rolling Convolution

· intro: CVPR 2017. SenseTime

· keywords: Recurrent Rolling Convolution (RRC)

· arxiv: https://arxiv.org/abs/1704.05776

· github: https://github.com/xiaohaoChen/rrc_detection

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

· intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc

· arxiv: https://arxiv.org/abs/1705.05922

Point Linking Network for Object Detection

· intro: Point Linking Network (PLN)

· arxiv: https://arxiv.org/abs/1706.03646

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

· intro: CVPR 2017

· arxiv: https://arxiv.org/abs/1707.01691

· github: https://github.com/taokong/RON

Mimicking Very Efficient Network for Object Detection

· intro: CVPR 2017. SenseTime & Beihang University

· paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf

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

· intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC

· arxiv: https://arxiv.org/abs/1707.06175

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

· intro: ICCV 2017

· arxiv: https://arxiv.org/abs/1707.06399

Recurrent Scale Approximation for Object Detection in CNN

· intro: ICCV 2017

· keywords: Recurrent Scale Approximation (RSA)

· arxiv: https://arxiv.org/abs/1707.09531

· github: https://github.com/sciencefans/RSA-for-object-detection

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

· 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

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

· arxiv:https://arxiv.org/abs/1712.00886

· github:https://github.com/szq0214/GRP-DSOD

RetinaNet

Focal Loss for Dense Object Detection

· intro: ICCV 2017 Best student paper award. Facebook AI Research

· keywords: RetinaNet

· arxiv: https://arxiv.org/abs/1708.02002

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

· intro: ICCV 2017

· arxiv: https://arxiv.org/abs/1708.02863

Incremental Learning of Object Detectors without Catastrophic Forgetting

· intro: ICCV 2017. Inria

· arxiv: https://arxiv.org/abs/1708.06977

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

· intro: NTU, Singapore & Amazon

· keywords: multi-instance multi-label domain adaption learning framework

· arxiv: https://arxiv.org/abs/1711.05954

MegDet

 MegDet: A Large Mini-Batch Object Detector

· arxiv: https://arxiv.org/abs/1711.07240

   Single-Shot Refinement Neural Network for Object Detection

· arxiv: https://arxiv.org/abs/1711.06897

· github: https://github.com/sfzhang15/RefineDet

Receptive Field Block Net for Accurate and Fast Object Detection

· arxiv: https://arxiv.org/abs/1711.07767

· github: https://github.com//ruinmessi/RFBNet

An Analysis of Scale Invariance in Object Detection - SNIP

· arxiv: https://arxiv.org/abs/1711.08189

· github: https://github.com/bharatsingh430/snip

Feature Selective Networks for Object Detection

https://arxiv.org/abs/1711.08879

Learning a Rotation Invariant Detector with Rotatable Bounding Box

· arxiv: https://arxiv.org/abs/1711.09405

· github: https://github.com/liulei01/DRBox

Scalable Object Detection for Stylized Objects

· intro: Microsoft AI & Research Munich

· arxiv: https://arxiv.org/abs/1711.09822

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

· arxiv: https://arxiv.org/abs/1712.00886

· github: https://github.com/szq0214/GRP-DSOD

Deep Regionlets for Object Detection

· keywords: region selection network, gating network

· arxiv: https://arxiv.org/abs/1712.02408

Training and Testing Object Detectors with Virtual Images

· intro: IEEE/CAA Journal of Automatica Sinica

· arxiv: https://arxiv.org/abs/1712.08470

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

· keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation

· arxiv: https://arxiv.org/abs/1712.08832

Spot the Difference by Object Detection

· intro: Tsinghua University & JD Group

· arxiv: https://arxiv.org/abs/1801.01051

Localization-Aware Active Learning for Object Detection

· arxiv: https://arxiv.org/abs/1801.05124

Object Detection with Mask-based Feature Encoding

https://arxiv.org/abs/1802.03934

LSTD: A Low-Shot Transfer Detector for Object Detection

· intro: AAAI 2018

· arxiv: https://arxiv.org/abs/1803.01529

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

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

Zero-Shot Detection

· intro: Australian National University

· keywords: YOLO

· arxiv: https://arxiv.org/abs/1803.07113

Learning Region Features for Object Detection

· intro: Peking University & MSRA

· arxiv: https://arxiv.org/abs/1803.07066

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

· intro: Singapore Management University & Zhejiang University

· arxiv: https://arxiv.org/abs/1803.08208

Object Detection for Comics using Manga109 Annotations

· intro: University of Tokyo & National Institute of Informatics, Japan

· arxiv: https://arxiv.org/abs/1803.08670

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

https://arxiv.org/abs/1803.11316

Transferring Common-Sense Knowledge for Object Detection

https://arxiv.org/abs/1804.01077

Multi-scale Location-aware Kernel Representation for Object Detection

· intro: CVPR 2018

· arxiv: https://arxiv.org/abs/1804.00428

· github: https://github.com/Hwang64/MLKP

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

· intro: National University of Defense Technology

· arxiv: https://arxiv.org/abs/1804.04606

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://arxiv.org/abs/1804.05810

DetNet

 DetNet: A Backbone network for Object Detection

arxiv: https://arxiv.org/abs/1804.06215

LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs

· arxiv: https://arxiv.org/abs/1805.04902

· github: https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection

ZSD

Zero-Shot Object Detection

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

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

· arxiv: https://arxiv.org/abs/1803.06049

Zero-Shot Object Detection by Hybrid Region Embedding

· arxiv: https://arxiv.org/abs/1805.06157

https://github.com/amusi/awesome-object-detection

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特征工程(一)

特征工程(二) :文本数据的展开、过滤和分块

特征工程(三):特征缩放,从词袋到 TF-IDF

特征工程(四): 类别特征

特征工程(五): PCA 降维

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深度学习、机器学习、数据分析、python

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