[深度学习]资源汇总

[深度学习]资源汇总



转自：.html#t-cnn

Method	VOC2007	VOC2010	VOC2012	ILSVRC 2013	MSCOCO 2015	Speed
OverFeat				24.3%
R-CNN (AlexNet)	58.5%	53.7%	53.3%	31.4%
R-CNN (VGG16)	66.0%
SPP_net(ZF-5)	54.2%(1-model), 60.9%(2-model)			31.84%(1-model), 35.11%(6-model)
DeepID-Net	64.1%			50.3%
NoC	73.3%		68.8%
Fast-RCNN (VGG16)	70.0%	68.8%	68.4%		19.7%(@[0.5-0.95]), 35.9%(@0.5)
MR-CNN	78.2%		73.9%
Faster-RCNN (VGG16)	78.8%		75.9%		21.9%(@[0.5-0.95]), 42.7%(@0.5)	198ms
Faster-RCNN (ResNet-101)	85.6%		83.8%		37.4%(@[0.5-0.95]), 59.0%(@0.5)
SSD300 (VGG16)	77.2%		75.8%		25.1%(@[0.5-0.95]), 43.1%(@0.5)	46 fps
SSD512 (VGG16)	79.8%		78.5%		28.8%(@[0.5-0.95]), 48.5%(@0.5)	19 fps
ION	79.2%		76.4%
CRAFT	75.7%		71.3%	48.5%
OHEM	78.9%		76.3%		25.5%(@[0.5-0.95]), 45.9%(@0.5)
R-FCN (ResNet-50)	77.4%					0.12sec(K40), 0.09sec(TitianX)
R-FCN (ResNet-101)	79.5%					0.17sec(K40), 0.12sec(TitianX)
R-FCN (ResNet-101),multi sc train	83.6%		82.0%		31.5%(@[0.5-0.95]), 53.2%(@0.5)
PVANet 9.0	89.8%		84.2%			750ms(CPU), 46ms(TitianX)

Leaderboard

Detection Results: VOC2012

• intro: Competition “comp4” (train on additional data)
• homepage: :8080/leaderboard/displaylb.php?challengeid=11&compid=4

Papers

Deep Neural Networks for Object Detection

• paper: .pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

• arxiv: .6229
• github:
• code: .php?id=software:overfeat:start

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv: .2524
• supp: .pdf
• slides: .pdf
• slides: .pdf
• github:
• notes: /
• caffe-pr(“Make R-CNN the Caffe detection example”):

MultiBox

Scalable Object Detection using Deep Neural Networks

• intro: first MultiBox. Train a CNN to predict Region of Interest.
• arxiv: .2249
• github:
• blog: .html

Scalable, High-Quality Object Detection

• intro: second MultiBox
• arxiv: .1441
• github:

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

• intro: ECCV 2014 / TPAMI 2015
• arxiv: .4729
• github:
• notes: /

DeepID-Net

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: /%CB%9Cwlouyang/projects/imagenetDeepId/index.html
• arxiv: .5661

Object Detectors Emerge in Deep Scene CNNs

• arxiv: .6856
• paper: .pdf
• paper: .pdf
• slides: .pdf

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

• intro: CVPR 2015
• project(code+data): .html
• arxiv: .04275
• github:

NoC

Object Detection Networks on Convolutional Feature Maps

• intro: TPAMI 2015
• arxiv: .06066

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

• arxiv: .03293
• slides: .pdf
• github:

Fast R-CNN

Fast R-CNN

• arxiv: .08083
• slides: .pdf
• github:
• github(COCO-branch):
• webcam demo:
• notes: /
• notes:
• github(“Fast R-CNN in MXNet”):
• github:
• github:
• github(Tensorflow):

DeepBox

DeepBox: Learning Objectness with Convolutional Networks

• arxiv: .02146
• github:

MR-CNN

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

• intro: ICCV 2015. MR-CNN
• arxiv: .01749
• github:
• notes: /
• notes: /
• 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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• intro: NIPS 2015
• arxiv: .01497
• gitxiv:
• slides: .pdf
• github(official, Matlab):
• github:
• github:
• github: .torch
• github:
• github:
• github:
• github(C++ demo):
• github:

Faster R-CNN in MXNet with distributed implementation and data parallelization

• github:

Contextual Priming and Feedback for Faster R-CNN

• intro: ECCV 2016. Carnegie Mellon University
• paper: .pdf
• poster: .pdf

An Implementation of Faster RCNN with Study for Region Sampling

• intro: Technical Report, 3 pages. CMU
• arxiv: .02138
• github:

YOLO

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

• arxiv: .02640
• code: /
• github:
• blog: /
• slides: =false&loop=false&delayms=3000&slide=id.p
• reddit: /
• github:
• github:
• github:
• github:
• github: .torch
• github:
• gtihub:

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

• blog:
• github:

Start Training YOLO with Our Own Data

• intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
• blog: /
• github:

R-CNN minus R

• arxiv: .06981

AttentionNet

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: .07704
• slides: .pdf
• slides: .pdf

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

• arxiv: .04874
• demo:
• KITTI result: .php

SSD

SSD: Single Shot MultiBox Detector

• intro: ECCV 2016 Oral
• arxiv: .02325
• paper: .pdf
• slides: /%7Ewliu/papers/ssd_eccv2016_slide.pdf
• github:
• video:
• github:
• github: .cpp
• github:
• github:
• github: .pytorch

Inside-Outside Net (ION)

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

• 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: .04143
• slides: .pdf
• coco-leaderboard:

Adaptive Object Detection Using Adjacency and Zoom Prediction

• intro: CVPR 2016. AZ-Net
• arxiv: .07711
• github:
• youtube: =YmFtuNwxaNM

G-CNN

G-CNN: an Iterative Grid Based Object Detector

• arxiv: .07729

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

• intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
• project page: .html
• arxiv: .05150

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

• arxiv: .08405

HyperNet

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

• arxiv: .00600

MultiPathNet

A MultiPath Network for Object Detection

• intro: BMVC 2016. Facebook AI Research (FAIR)
• arxiv: .02135
• github:

CRAFT

CRAFT Objects from Images

• intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
• project page: .html
• arxiv: .03239
• paper: .pdf
• github:

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

• intro: CVPR 2016 Oral. Online hard example mining (OHEM)
• arxiv: .03540
• paper: .pdf
• github（Official）:
• author page: /

Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

• intro: CVPR 2016
• arxiv: .05766

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

• intro: scale-dependent pooling (SDP), cascaded rejection clas-sifiers (CRC)
• paper: .pdf

R-FCN

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

• arxiv: .06409
• github:
• github:
• github(PyTorch):
• github:

Weakly supervised object detection using pseudo-strong labels

• arxiv: .04731

Recycle deep features for better object detection

• arxiv: .05066

MS-CNN

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: .07155
• github:
• poster: .pdf

Multi-stage Object Detection with Group Recursive Learning

• intro: VOC2007: 78.6%, VOC2012: 74.9%
• arxiv: .05159

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

• intro: WACV 2017. SubCNN
• arxiv: .04693
• github:

PVANET

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: .08021
• github:
• leaderboard(PVANet 9.0): :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: .08588

GBD-Net

Gated Bi-directional CNN for Object Detection

• intro: The Chinese University of Hong Kong & Sensetime Group Limited
• paper: .1007/978-3-319-46478-7_22
• mirror:

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: .02579
• github:

StuffNet

StuffNet: Using ‘Stuff’ to Improve Object Detection

• arxiv: .05861

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

• arxiv: .09609

Hierarchical Object Detection with Deep Reinforcement Learning

• intro: Deep Reinforcement Learning Workshop (NIPS 2016)
• project page: /
• arxiv: .03718
• slides:
• github:
• blog: /

Learning to detect and localize many objects from few examples

• arxiv: .05664

Speed/accuracy trade-offs for modern convolutional object detectors

• intro: Google Research
• arxiv: .10012

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

• arxiv: .01051
• github:

Feature Pyramid Network (FPN)

Feature Pyramid Networks for Object Detection

• intro: Facebook AI Research
• arxiv: .03144

Action-Driven Object Detection with Top-Down Visual Attentions

• arxiv: .06704

Beyond Skip Connections: Top-Down Modulation for Object Detection

• intro: CMU & UC Berkeley & Google Research
• arxiv: .06851

YOLOv2

YOLO9000: Better, Faster, Stronger

• arxiv: .08242
• code: /
• github(Chainer):
• github(Keras):
• github(PyTorch):
• github(Tensorflow):
• github(Windows):
• github:

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

• github:

DSSD

DSSD : Deconvolutional Single Shot Detector

• intro: UNC Chapel Hill & Amazon Inc
• arxiv: .06659

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

• intro: Inha University
• arxiv: .01243

Attentional Network for Visual Object Detection

• intro: University of Maryland & Mitsubishi Electric Research Laboratories
• arxiv: .01478

CC-Net

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

• intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
• arxiv: .07054

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

.10295

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

• intro: CVPR 2017
• paper: .pdf
• github(Caffe):

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

• intro: CVPR 2017
• arxiv: .03944

Spatial Memory for Context Reasoning in Object Detection

• arxiv: .04224

Improving Object Detection With One Line of Code

• intro: University of Maryland
• keywords: Soft-NMS
• arxiv: .04503
• github:

Accurate Single Stage Detector Using Recurrent Rolling Convolution

• intro: CVPR 2017
• arxiv: .05776
• github:

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

.05775

Detection From Video

Learning Object Class Detectors from Weakly Annotated Video

• intro: CVPR 2012
• paper: .pdf

Analysing domain shift factors between videos and images for object detection

• arxiv: .01186

Video Object Recognition

• slides: .pptx

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:

T-CNN

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

• intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
• arxiv: .02532
• github:

Object Detection from Video Tubelets with Convolutional Neural Networks

• intro: CVPR 2016 Spotlight paper
• arxiv: .04053
• paper: .pdf
• gihtub:

Object Detection in Videos with Tubelets and Multi-context Cues

• intro: SenseTime Group
• slides: .pdf
• slides: .pdf

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

• intro: BMVC 2016
• keywords: pseudo-labeler
• arxiv: .04648
• paper: .pdf

CNN Based Object Detection in Large Video Images

• intro: WangTao @ 爱奇艺
• keywords: object retrieval, object detection, scene classification
• slides: .pdf

Object Detection in Videos with Tubelet Proposal Networks

• arxiv: .06355

Flow-Guided Feature Aggregation for Video Object Detection

• intro: MSRA
• arxiv: .10025

Video Object Detection using Faster R-CNN

• blog: /
• github:

Object Detection in 3D

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

• arxiv: .06666

Object Detection on RGB-D

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

• arxiv: .5736

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

• intro: CVPR 2016
• paper: .html

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

.03347

Salient Object Detection

This task involves predicting the salient regions of an image given by human eye fixations.

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

.html

Large-scale optimization of hierarchical features for saliency prediction in natural images

• paper: .pdf

Predicting Eye Fixations using Convolutional Neural Networks

• paper: =72648

Saliency Detection by Multi-Context Deep Learning

• paper: .pdf

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

• arxiv: .05484

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

• paper: www.shengfenghe/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

Shallow and Deep Convolutional Networks for Saliency Prediction

• arxiv: .00845
• github:

Recurrent Attentional Networks for Saliency Detection

• intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
• arxiv: .03227

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

• arxiv: .04730

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

• intro: CVPR 2016
• project page: .html
• paper: .pdf
• github:
• caffe model zoo:

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

• paper: .pdf

Salient Object Subitizing

• intro: CVPR 2015
• intro: predicting the existence and the number of salient objects in an image using holistic cues
• project page: .html
• arxiv: .07525
• paper: .pdf
• caffe model zoo:

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

• intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
• arxiv: .05177

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

• intro: ECCV 2016
• arxiv: .05186

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

• arxiv: .08029

A Deep Multi-Level Network for Saliency Prediction

• arxiv: .01064

Visual Saliency Detection Based on Multiscale Deep CNN Features

• intro: IEEE Transactions on Image Processing
• arxiv: .02077

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

• intro: DSCLRCN
• arxiv: .01708

Deeply supervised salient object detection with short connections

• arxiv: .04849

Weakly Supervised Top-down Salient Object Detection

• intro: Nanyang Technological University
• arxiv: .05345

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

• project page: /
• arxiv: .01081

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

• arxiv: .00372

A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network

• arxiv: .00615

Saliency Detection by Forward and Backward Cues in Deep-CNNs

.00152

Supervised Adversarial Networks for Image Saliency Detection

.07242

Saliency Detection in Video

Deep Learning For Video Saliency Detection

• arxiv: .00871

Visual Relationship Detection

Visual Relationship Detection with Language Priors

• intro: ECCV 2016 oral
• paper: .pdf
• github:

ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

• intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
• arxiv: .07191

Visual Translation Embedding Network for Visual Relation Detection

• arxiv: .08319

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

• intro: CVPR 2017 spotlight paper
• arxiv: .03054

Detecting Visual Relationships with Deep Relational Networks

• intro: CVPR 2017 oral. The Chinese University of Hong Kong
• arxiv: .03114

Specific Object Deteciton

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

• intro: Yahoo
• arxiv: .02766
• github:

From Facial Parts Responses to Face Detection: A Deep Learning Approach

• project page: .html

Compact Convolutional Neural Network Cascade for Face Detection

• arxiv: .01292
• github:

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

• intro: ECCV 2016
• arxiv: .00850
• github(MXNet):

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

• intro: CMU
• arxiv: .05413

Finding Tiny Faces

• intro: CMU
• project page: .html
• arxiv: .04402
• github:

Towards a Deep Learning Framework for Unconstrained Face Detection

• intro: overlap with CMS-RCNN
• arxiv: .05322

Supervised Transformer Network for Efficient Face Detection

• arxiv: .05477

UnitBox

UnitBox: An Advanced Object Detection Network

• intro: ACM MM 2016
• arxiv: .01471

Bootstrapping Face Detection with Hard Negative Examples

• author: 万韶华 @ 小米.
• intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
• arxiv: .02236

Grid Loss: Detecting Occluded Faces

• intro: ECCV 2016
• arxiv: .00129
• paper: .pdf
• poster: .pdf

A Multi-Scale Cascade Fully Convolutional Network Face Detector

• intro: ICPR 2016
• arxiv: .03536

MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

• project page: .html
• arxiv: .02878
• github(Matlab):
• github:
• github:
• github(MXNet):
• github:
• github(Caffe):
• github:

Face Detection using Deep Learning: An Improved Faster RCNN Approach

• intro: DeepIR Inc
• arxiv: .08289

Faceness-Net: Face Detection through Deep Facial Part Responses

• intro: An extended version of ICCV 2015 paper
• arxiv: .08393

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”

• intro: CVPR 2017. MP-RCNN, MP-RPN
• arxiv: .09145

End-To-End Face Detection and Recognition

.10818

Facial Point / Landmark Detection

Deep Convolutional Network Cascade for Facial Point Detection

• homepage: .htm
• paper: .pdf
• github:

Facial Landmark Detection by Deep Multi-task Learning

• intro: ECCV 2014
• project page: .html
• paper: .pdf
• github(Matlab):

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

• intro: ECCV 2016
• arxiv: .05477

Detecting facial landmarks in the video based on a hybrid framework

• arxiv: .06441

Deep Constrained Local Models for Facial Landmark Detection

• arxiv: .08657

Effective face landmark localization via single deep network

• arxiv: .02719

A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

.01880

People Detection

End-to-end people detection in crowded scenes

• arxiv: .04878
• github:
• ipn: .ipynb

Detecting People in Artwork with CNNs

• intro: ECCV 2016 Workshops
• arxiv: .08871

Deep Multi-camera People Detection

• arxiv: .04593

Person Head Detection

Context-aware CNNs for person head detection

• arxiv: .07917
• github:

Pedestrian Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

• intro: CVPR 2015
• project page: /
• paper: .0069

Deep Learning Strong Parts for Pedestrian Detection

• intro: ICCV 2015. CUHK. DeepParts
• intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
• paper: .pdf

Deep convolutional neural networks for pedestrian detection

• arxiv: .03608
• github:

Scale-aware Fast R-CNN for Pedestrian Detection

• arxiv: .08160

New algorithm improves speed and accuracy of pedestrian detection

• blog: –nai020516.php

Pushing the Limits of Deep CNNs for Pedestrian Detection

• intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
• arxiv: .04525

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

• arxiv: .04436

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

• arxiv: .04441

Is Faster R-CNN Doing Well for Pedestrian Detection?

• intro: ECCV 2016
• arxiv: .07032
• github:

Reduced Memory Region Based Deep Convolutional Neural Network Detection

• intro: IEEE 2016 ICCE-Berlin
• arxiv: .02500

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

• arxiv: .03466

Multispectral Deep Neural Networks for Pedestrian Detection

• intro: BMVC 2016 oral
• arxiv: .02644

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

• intro: CVPR 2017
• project page: :5000/publications/synunity/
• arxiv: .06283
• github(Tensorflow):

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

• intro: ECCV 2016
• arxiv: .04564

Evolving Boxes for fast Vehicle Detection

• arxiv: .00254

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

• project page(code+dataset): /
• paper: 120.52.73.11/www.cv-foundation/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
• code & model: .zip

Boundary / Edge / Contour Detection

Holistically-Nested Edge Detection

• intro: ICCV 2015, Marr Prize
• paper: .pdf
• arxiv: .06375
• github:

Unsupervised Learning of Edges

• intro: CVPR 2016. Facebook AI Research
• arxiv: .04166
• zn-blog: .html

Pushing the Boundaries of Boundary Detection using Deep Learning

• arxiv: .07386

Convolutional Oriented Boundaries

• intro: ECCV 2016
• arxiv: .02755

Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

• project page: /
• arxiv: .04658
• github:

Richer Convolutional Features for Edge Detection

• intro: richer convolutional features (RCF)
• arxiv: .02103

Skeleton Detection

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

• arxiv: .09446
• github:

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

• arxiv: .03659

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

• intro: CVPR 2017
• arxiv: .02243
• github:

Fruit Detection

Deep Fruit Detection in Orchards

• arxiv: .03677

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

• intro: The Journal of Field Robotics in May 2016
• project page: /
• arxiv: .08120

Part Detection

Objects as context for part detection

.09529

Others

Deep Deformation Network for Object Landmark Localization

• arxiv: .01014

Fashion Landmark Detection in the Wild

• intro: ECCV 2016
• project page: .html
• arxiv: .03049
• github(Caffe):

Deep Learning for Fast and Accurate Fashion Item Detection

• intro: Kuznech Inc.
• intro: MultiBox and Fast R-CNN
• paper: .pdf

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)

• github:

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

• intro: IEEE SITIS 2016
• arxiv: .04357

Associative Embedding:End-to-End Learning for Joint Detection and Grouping

• arxiv: .05424

Deep Cuboid Detection: Beyond 2D Bounding Boxes

• intro: CMU & Magic Leap
• arxiv: .10010

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

• arxiv: .03019

Deep Learning Logo Detection with Data Expansion by Synthesising Context

• arxiv: .09322

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

• arxiv: .00307

Automatic Handgun Detection Alarm in Videos Using Deep Learning

• arxiv: .05147
• results:

Object Proposal

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

• arxiv: .04445
• github:

Scale-aware Pixel-wise Object Proposal Networks

• intro: IEEE Transactions on Image Processing
• arxiv: .04798

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

• intro: BMVC 2016. AttractioNet
• arxiv: .04446
• github:

Learning to Segment Object Proposals via Recursive Neural Networks

• arxiv: .01057

Learning Detection with Diverse Proposals

• intro: CVPR 2017
• keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
• arxiv: .03533

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

• keywords: product detection
• arxiv: .06752

Improving Small Object Proposals for Company Logo Detection

• intro: ICMR 2017
• arxiv: .08881

Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

• intro: PhD Thesis
• homepage: .html
• phd-thesis: .pdf
• github(“SDS using hypercolumns”):

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

• arxiv: .00949

Weakly Supervised Object Localization Using Size Estimates

• arxiv: .04314

Active Object Localization with Deep Reinforcement Learning

• intro: ICCV 2015
• keywords: Markov Decision Process
• arxiv: .06015

Localizing objects using referring expressions

• intro: ECCV 2016
• keywords: LSTM, multiple instance learning (MIL)
• paper: .pdf
• github:

LocNet: Improving Localization Accuracy for Object Detection

• arxiv: .07763
• github:

Learning Deep Features for Discriminative Localization

• homepage: /
• arxiv: .04150
• github(Tensorflow):
• github:
• github:

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

• intro: ECCV 2016
• project page: /
• arxiv: .04331
• github:

Tutorials / Talks

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

• slides: .pdf

Towards Good Practices for Recognition & Detection

• intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
• slides: .pdf

Projects

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 theReInspect algorithm”
• github:

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

• github: .torch

Using DIGITS to train an Object Detection network

• github: .md

FCN-MultiBox Detector

• intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
• github: .torch

KittiBox: A car detection model implemented in Tensorflow.

• keywords: MultiNet
• intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
• github:

Tools

BeaverDam: Video annotation tool for deep learning training labels

Blogs

Convolutional Neural Networks for Object Detection

/

Introducing automatic object detection to visual search (Pinterest)

• keywords: Faster R-CNN
• blog:
• demo: .mp4
• review: /?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

Deep Learning for Object Detection with DIGITS

• blog: /

Analyzing The Papers Behind Facebook’s Computer Vision Approach

• keywords: DeepMask, SharpMask, MultiPathNet
• blog: .html

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

• blog: /
• github:

Object Detection in Satellite Imagery, a Low Overhead Approach

• part 1: .2csh4iwx9
• part 2: .f9b7dgf64

You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

• part 1: .fmmi2o3of
• part 2: .nwzarsz1t

Faster R-CNN Pedestrian and Car Detection

• blog: /
• ipn:
• github:

Small U-Net for vehicle detection

• blog: /@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad

Region of interest pooling explained

• blog: /
• github: