【目标识别】深度学习进行目标识别的资源列表

【目标识别】深度学习进行目标识别的资源列表：O网页链接包括RNN、MultiBox、SPP-Net、DeepID-Net、Fast R-CNN、DeepBox、MR-CNN、Faster R-CNN、YOLO、DenseBox、SSD、Inside-Outside Net、G-CNN等。
Papers

Deep Neural Networks for Object Detection

paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

[td]

method	ILSVRC 2013 mAP
OverFeat	24.3%

intro: A deep version of the sliding window method, predicts bounding box directly from each location of the topmost feature map after knowing the confidences of the underlying object categories.
arXiv: http://arxiv.org/abs/1312.6229
code: https://github.com/sermanet/OverFeat

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation(R-CNN)

[td]

method	VOC 2007 mAP	VOC 2010 mAP	VOC 2012 mAP	ILSVRC 2013 mAP
R-CNN,AlexNet	54.2%	50.2%	49.6%
R-CNN,bbox reg,AlexNet	58.5%	53.7%	53.3%	31.4%
R-CNN,bbox reg,ZFNet	59.2%
R-CNN,VGG-Net	62.2%
R-CNN,bbox reg,VGG-Net	66.0%

arXiv: http://arxiv.org/abs/1311.2524
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
code: 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

MultiBox

Scalable Object Detection using Deep Neural Networks (MultiBox)

intro: Train a CNN to predict Region of Interest.
arXiv: http://arxiv.org/abs/1312.2249
code: https://github.com/google/multibox

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

[td]

method	VOC 2007 mAP	ILSVRC 2013 mAP
SPP_net(ZF-5),1-model	54.2%	31.84%
SPP_net(ZF-5),2-model	60.9%
SPP_net(ZF-5),6-model		35.11%

arXiv: http://arxiv.org/abs/1406.4729
code: https://github.com/ShaoqingRen/SPP_net
notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/

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

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

Scalable, High-Quality Object Detection

arXiv: http://arxiv.org/abs/1412.1441
code: https://github.com/google/multibox

DeepID-Net

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

[td]

method	VOC 2007 mAP	ILSVRC 2013 mAP
DeepID-Net	64.1%	50.3%

arXiv: http://arxiv.org/abs/1412.5661

Object Detection Networks on Convolutional Feature Maps

[td]

method	Trained on	mAP
NoC	07+12	68.8%
NoC,bb	07+12	71.6%
NoC,+EB	07+12	71.8%
NoC,+EB,bb	07+12	73.3%

arXiv: http://arxiv.org/abs/1504.06066

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

[td]

Model	BBoxReg?	VOC 2007 mAP(IoU>0.5)
R-CNN(AlexNet)	No	54.2%
R-CNN(VGG)	No	60.6%
+StructObj	No	61.2%
+StructObj-FT	No	62.3%
+FGS	No	64.8%
+StructObj+FGS	No	65.9%
+StructObj-FT+FGS	No	66.5%

[td]

Model	BBoxReg?	VOC 2007 mAP(IoU>0.5)
R-CNN(AlexNet)	Yes	58.5%
R-CNN(VGG)	Yes	65.4%
+StructObj	Yes	66.6%
+StructObj-FT	Yes	66.9%
+FGS	Yes	67.2%
+StructObj+FGS	Yes	68.5%
+StructObj-FT+FGS	Yes	68.4%

Fast R-CNN

[td]

method	data	VOC 2007 mAP
FRCN,VGG16	07	66.9%
FRCN,VGG16	07+12	70.0%

[td]

method	data	VOC 2010 mAP
FRCN,VGG16	12	66.1%
FRCN,VGG16	07++12	68.8%

[td]

method	data	VOC 2012 mAP
FRCN,VGG16	12	65.7%
FRCN,VGG16	07++12	68.4%

arXiv: http://arxiv.org/abs/1504.08083
slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
github: https://github.com/rbgirshick/fast-rcnn
webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
notes: http://blog.youkuaiyun.com/linj_m/article/details/48930179
github(“Train Fast-RCNN on Another Dataset”): https://github.com/zeyuanxy/fast-rcnn/tree/master/help/train

DeepBox

DeepBox: Learning Objectness with Convolutional Networks

arXiv: http://arxiv.org/abs/1505.02146
github: https://github.com/weichengkuo/DeepBox

MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model (MR-CNN)

[td]

Model	Trained on	VOC 2007 mAP
VGG-net	07+12	78.2%
VGG-net	07	74.9%

[td]

Model	Trained on	VOC 2012 mAP
VGG-net	07+12	73.9%
VGG-net	12	70.7%

arXiv: http://arxiv.org/abs/1505.01749
code: “Pdf and code will appear here shortly – stay tuned”
http://imagine.enpc.fr/~komodakn/
notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks(NIPS 2015)

[td]

	training data	test data	mAP	time/img
Faster RCNN, VGG-16	07	VOC 2007 test	69.9%	198ms
Faster RCNN, VGG-16	07+12	VOC 2007 test	73.2%	198ms
Faster RCNN, VGG-16	12	VOC 2007 test	67.0%	198ms
Faster RCNN, VGG-16	07++12	VOC 2007 test	70.4%	198ms

arXiv: http://arxiv.org/abs/1506.01497
github: https://github.com/ShaoqingRen/faster_rcnn
github: https://github.com/rbgirshick/py-faster-rcnn

YOLO

You Only Look Once: Unified, Real-Time Object Detection(YOLO)

intro: YOLO uses the whole topmost feature map to predict both confidences for multiple categories and bounding boxes (which are shared for these categories).
arXiv: http://arxiv.org/abs/1506.02640
code: http://pjreddie.com/darknet/yolo/
github: https://github.com/pjreddie/darknet
reddit:https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
github(YOLO_tensorflow): https://github.com/gliese581gg/YOLO_tensorflow

R-CNN minus R

arXiv: http://arxiv.org/abs/1506.06981

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

arXiv: http://arxiv.org/abs/1509.04874
demo: http://pan.baidu.com/s/1mgoWWsS
KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php

SSD

SSD: Single Shot MultiBox Detector

arXiv: http://arxiv.org/abs/1512.02325
github: https://github.com/weiliu89/caffe/tree/ssd
video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973

Inside-Outside Net

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Detection results on VOC 2007 test:

[td]

Method	R	S	W	D	Train	mAP
FRCN					07+12	70.0
RPN					07+12	73.2
MR-CNN			√		07+12	78.2
ION					07+12	74.6
ION	√				07+12	75.6
ION	√	√			07+12+S	76.5
ION	√	√	√		07+12+S	78.5
ION	√	√	√	√	07+12+S	79.2

Detection results on VOC 2012 test:

[td]

Method	R	S	W	D	Train	mAP
FRCN					07++12	68.4
RPN					07++12	70.4
FRCN+YOLO					07++12	70.4
HyperNet					07++12	71.4
MR-CNN			√		07+12	73.9
ION	√	√	√	√	07+12+S	76.4

intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
arxiv: http://arxiv.org/abs/1512.04143
slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard

G-CNN

G-CNN: an Iterative Grid Based Object Detector

arxiv: http://arxiv.org/abs/1512.07729

Learning Deep Features for Discriminative Localization

homepage: http://cnnlocalization.csail.mit.edu/
arxiv: http://arxiv.org/abs/1512.04150
github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector

Factors in Finetuning Deep Model for object detection

arxiv: http://arxiv.org/abs/1601.05150

We don’t need no bounding-boxes: Training object class detectors using only human verification

arxiv: http://arxiv.org/abs/1602.08405

A MultiPath Network for Object Detection

arxiv: http://arxiv.org/abs/1604.02135

Beyond Bounding Boxes: Precise Localization of Objects in Images (PhD Thesis)

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

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

arxiv: http://arxiv.org/abs/1604.02532
github: https://github.com/myfavouritekk/T-CNN

Training Region-based Object Detectors with Online Hard Example Mining

arxiv: http://arxiv.org/abs/1604.03540

Specific Object Deteciton

End-to-end people detection in crowded scenes

Tutorials

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

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

Codes

TensorBox: a simple framework for training neural networks to detect objects in images

intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
github: https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch