目标检测领域 2015-1
https://handong1587.github.io/deep_learning/2015/10/09/nlp.html
intro: Competition “comp4” (train on own data)
homepage:http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
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
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.
intro: training a convolutional network to simultaneously classify, locate and detect objects in images can boost the classification accuracy and the detection and localization accuracy of all tasks
arxiv:http://arxiv.org/abs/1312.6229
github:https://github.com/sermanet/OverFeat
code:http://cilvr.nyu.edu/doku.php?id=software:overfeat:start
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
Scalable Object Detection using Deep Neural Networks
intro: MultiBox. Train a CNN to predict Region of Interest.
arxiv:http://arxiv.org/abs/1312.2249
github:https://github.com/google/multibox
blog:https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html
Scalable, High-Quality Object Detection
intro: MultiBox
arxiv:http://arxiv.org/abs/1412.1441
github:https://github.com/google/multibox
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/
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
arxiv:http://arxiv.org/abs/1407.5736
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
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
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
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
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(Tensorflow):https://github.com/zplizzi/tensorflow-fast-rcnn
DeepBox: Learning Objectness with Convolutional Networks
arxiv:http://arxiv.org/abs/1505.02146
github:https://github.com/weichengkuo/DeepBox
Object detection via a multi-region & semantic segmentation-aware CNN model
intro: ICCV 2015. MR-CNN
arxiv:http://arxiv.org/abs/1505.01749
github:https://github.com/gidariss/mrcnn-object-detection
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/
my notes: Who can tell me why there are a bunch of duplicated sentences in section 7.2 “Detection error analysis”? :-D
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
intro: NIPS 2015
arxiv:http://arxiv.org/abs/1506.01497
slides:http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
github:https://github.com/ShaoqingRen/faster_rcnn
github:https://github.com/rbgirshick/py-faster-rcnn
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
Faster R-CNN in MXNet with distributed implementation and data parallelization
github:https://github.com/dmlc/mxnet/tree/master/example/rcnn
You Only Look Once: Unified, Real-Time Object Detection
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: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
gtihub:https://github.com/AlexeyAB/yolo-windows
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
R-CNN minus R
arxiv:http://arxiv.org/abs/1506.06981
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
intro: ICCV 2015
intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
arxiv:http://arxiv.org/abs/1506.07704
slides:https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
slides:http://image-net.org/challenges/talks/lunit-kaist-slide.pdf
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: Single Shot MultiBox Detector
intro: BMVC 2016. Facebook AI Research (FAIR)
arxiv:http://arxiv.org/abs/1604.02135
github:https://github.com/facebookresearch/multipathnet
CRAFT Objects from Images
intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
project page:http://byangderek.github.io/projects/craft.html
arxiv:https://arxiv.org/abs/1604.03239
github:https://github.com/byangderek/CRAFT
Training Region-based Object Detectors with Online Hard Example Mining
intro: CVPR 2016 Oral. Online hard example mining (OHEM)
arxiv:http://arxiv.org/abs/1604.03540
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
intro: CVPR 2016
arxiv:http://arxiv.org/abs/1604.05766
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf
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:https://github.com/Orpine/py-R-FCN
Weakly supervised object detection using pseudo-strong labels
arxiv:http://arxiv.org/abs/1607.04731
Recycle deep features for better object detection
arxiv:http://arxiv.org/abs/1607.05066
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
intro: ECCV 2016
intro: 640×480: 15 fps, 960×720: 8 fps
arxiv:http://arxiv.org/abs/1607.07155
github:https://github.com/zhaoweicai/mscnn
poster:http://www.eccv2016.org/files/posters/P-2B-38.pdf
Multi-stage Object Detection with Group Recursive Learning
intro: VOC2007: 78.6%, VOC2012: 74.9%
arxiv:http://arxiv.org/abs/1608.05159
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
intro: SubCNN
arxiv:http://arxiv.org/abs/1604.04693
github:https://github.com/yuxng/SubCNN
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
intro: “less channels with more layers”, concatenated ReLU, Inception, and HyperNet, batch normalization, residual connections
arxiv:http://arxiv.org/abs/1608.08021
github:https://github.com/sanghoon/pva-faster-rcnn
leaderboard(PVANet 9.0):http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation ofarXiv:1608.08021
arxiv:https://arxiv.org/abs/1611.08588
Gated Bi-directional CNN for Object Detection
intro: The Chinese University of Hong Kong & Sensetime Group Limited
paper:http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
mirror:https://pan.baidu.com/s/1dFohO7v
Crafting GBD-Net for Object Detection
intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
intro: gated bi-directional CNN (GBD-Net)
arxiv:https://arxiv.org/abs/1610.02579
github:https://github.com/craftGBD/craftGBD
StuffNet: Using ‘Stuff’ to Improve Object Detection
arxiv:https://arxiv.org/abs/1610.05861
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
arxiv:https://arxiv.org/abs/1610.09609
Hierarchical Object Detection with Deep Reinforcement Learning
intro: Deep Reinforcement Learning Workshop (NIPS 2016)
project page:https://imatge-upc.github.io/detection-2016-nipsws/
arxiv:https://arxiv.org/abs/1611.03718
github:https://github.com/imatge-upc/detection-2016-nipsws
Learning to detect and localize many objects from few examples
arxiv:https://arxiv.org/abs/1611.05664
Speed/accuracy trade-offs for modern convolutional object detectors
intro: Google Research
arxiv:https://arxiv.org/abs/1611.10012
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
arxiv:https://arxiv.org/abs/1612.01051
Feature Pyramid Networks for Object Detection
intro: Facebook AI Research
arxiv:https://arxiv.org/abs/1612.03144
Learning Object Class Detectors from Weakly Annotated Video
intro: CVPR 2012
paper:https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf
Analysing domain shift factors between videos and images for object detection
arxiv:https://arxiv.org/abs/1501.01186
Video Object Recognition
Deep Learning for Saliency Prediction in Natural Video
intro: Submitted on 12 Jan 2016
keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
paper:https://hal.archives-ouvertes.fr/hal-01251614/document
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
arxiv:http://arxiv.org/abs/1604.02532
github:https://github.com/myfavouritekk/T-CNN
Object Detection from Video Tubelets with Convolutional Neural Networks
intro: CVPR 2016 Spotlight paper
arxiv:https://arxiv.org/abs/1604.04053
paper:http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
gihtub:https://github.com/myfavouritekk/vdetlib
Object Detection in Videos with Tubelets and Multi-context Cues
intro: SenseTime Group
slides:http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
intro: BMVC 2016
keywords: pseudo-labeler
arxiv:http://arxiv.org/abs/1607.04648
paper:http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf
CNN Based Object Detection in Large Video Images
intro: WangTao @ 爱奇艺
keywords: object retrieval, object detection, scene classification
YouTube-Objects dataset v2.2
homepage:http://calvin.inf.ed.ac.uk/datasets/youtube-objects-dataset/
ILSVRC2015: Object detection from video (VID)
homepage:http://vision.cs.unc.edu/ilsvrc2015/download-videos-3j16.php#vid
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
arxiv:https://arxiv.org/abs/1609.06666
This task involves predicting the salient regions of an image given by human eye fixations.
Best Deep Saliency Detection Models (CVPR 2016 & 2015)
http://i.cs.hku.hk/~yzyu/vision.html
Large-scale optimization of hierarchical features for saliency prediction in natural images
paper:http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf
Predicting Eye Fixations using Convolutional Neural Networks
paper:http://www.escience.cn/system/file?fileId=72648
Saliency Detection by Multi-Context Deep Learning
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
arxiv:http://arxiv.org/abs/1510.05484
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
