object-detection_loss rank mining-优快云博客

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

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

Start Training YOLO with Our Own Data

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

YOLOv2

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

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

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

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/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

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

intro: Peking University & Tsinghua University & Megvii Inc
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

intro: RFBNet
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