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目标检测（8）
本文转载自：http://blog.csdn.net/zhuiqiuk/article/details/
https://handong1587.github.io/deep_learning//nlp.html
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ILSVRC 2013
MSCOCO 2015
R-CNN (AlexNet)
R-CNN (VGG16)
SPP_net(ZF-5)
54.2%(1-model), 60.9%(2-model)
31.84%(1-model), 35.11%(6-model)
DeepID-Net
Fast-RCNN (VGG16)
19.7%(@[0.5-0.95]), 35.9%(@0.5)
Faster-RCNN (VGG16)
21.9%(@[0.5-0.95]), 42.7%(@0.5)
Faster-RCNN (ResNet-101)
37.4%(@[0.5-0.95]), 59.0%(@0.5)
SSD300 (VGG16)
SSD500 (VGG16)
22.3%(@[0.5-0.95]), 41.0%(@0.5)
25.5%(@[0.5-0.95]), 45.9%(@0.5)
R-FCN (ResNet-50)
0.12sec(K40), 0.09sec(TitianX)
R-FCN (ResNet-101)
0.17sec(K40), 0.12sec(TitianX)
R-FCN (ResNet-101),multi sc train
31.5%(@[0.5-0.95]), 53.2%(@0.5)
PVANet 9.0
750ms(CPU), 46ms(TitianX)
Detection Results: VOC2012
intro: Competition “comp4” (train on own data)homepage:&
Deep Neural Networks for Object Detection
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 tasksarxiv:&github:&code:&
Rich feature hierarchies for accurate object detection and semantic segmentation
intro: R-CNNarxiv:&supp:&slides:&slides:&github:&notes:&caffe-pr(“Make R-CNN the Caffe detection example”):
Scalable Object Detection using Deep Neural Networks
intro: MultiBox. Train a CNN to predict Region of Interest.arxiv:&github:&blog:&
Scalable, High-Quality Object Detection
intro: MultiBoxarxiv:&github:&
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
intro: ECCV 2014 / TPAMI 2015arxiv:&github:&notes:&
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
intro: PAMI 2016intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representationsproject page:arxiv:&
Object Detectors Emerge in Deep Scene CNNs
arxiv:&paper:&paper:&slides:&
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
intro: CVPR 2015project(code+data):&arxiv:&github:&
Object Detection Networks on Convolutional Feature Maps
intro: TPAMI 2015arxiv:&
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
arxiv:&slides:&github:&
Fast R-CNN
arxiv:&slides:&github:&webcam demo:&notes:&notes:&github(“Fast R-CNN in MXNet”):&github:&github:&github(Tensorflow):&
DeepBox: Learning Objectness with Convolutional Networks
arxiv:&github:&
Object detection via a multi-region & semantic segmentation-aware CNN model
intro: ICCV 2015. MR-CNNarxiv:&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: Towards Real-Time Object Detection with Region Proposal Networks
intro: NIPS 2015arxiv:&gitxiv:&slides:&github:&github:&github:&github(Torch):&github(Torch):&github(Tensorflow):&github(tensorflow):&
Faster R-CNN in MXNet with distributed implementation and data parallelization
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:&code:&github:&reddit:github:&github:&github:&github:&github:&github:&gtihub:&
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
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
intro: ICCV 2015intro: state-of-the-art performance of 65% (AP) on PASCAL VOC
human detection taskarxiv:&slides:&slides:&
DenseBox: Unifying Landmark Localization with End to End Object Detection
arxiv:&demo:&KITTI result:&
SSD: Single Shot MultiBox Detector
arxiv:&paper:&github:&video:&github(MXNet):&github:&github(Keras):&
为什么SSD(Single Shot MultiBox Detector)对小目标的检测效果不好？
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:&slides:&coco-leaderboard:&
Adaptive Object Detection Using Adjacency and Zoom Prediction
intro: CVPR 2016. AZ-Netarxiv:&github:&youtube:&
G-CNN: an Iterative Grid Based Object Detector
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 detectionproject page:arxiv:&
We don’t need no bounding-boxes: Training object class detectors using only human verification
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
A MultiPath Network for Object Detection
intro: BMVC 2016. Facebook AI Research (FAIR)arxiv:&github:&
CRAFT Objects from Images
intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNNproject page:&arxiv:&paper:&github:&
Training Region-based Object Detectors with Online Hard Example Mining
intro: CVPR 2016 Oral. Online hard example mining (OHEM)arxiv:&paper:&
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
intro: CVPR 2016arxiv:&
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
R-FCN: Object Detection via Region-based Fully Convolutional Networks
arxiv:&github:&github:&
Weakly supervised object detection using pseudo-strong labels
Recycle deep features for better object detection
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
intro: ECCV 2016intro: 640×480: 15 fps, 960×720: 8 fpsarxiv:&github:&poster:&
Multi-stage Object Detection with Group Recursive Learning
intro: VOC%, VOC%arxiv:&
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
intro: SubCNNarxiv:&github:&
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 connectionsarxiv:&github:&leaderboard(PVANet 9.0):&
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 of&arxiv:&
Gated Bi-directional CNN for Object Detection
intro: The Chinese University of Hong Kong & Sensetime Group Limitedpaper:&mirror:&
Crafting GBD-Net for Object Detection
intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideointro: gated bi-directional CNN (GBD-Net)arxiv:&github:&
StuffNet: Using ‘Stuff’ to Improve Object Detection
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
Hierarchical Object Detection with Deep Reinforcement Learning
intro: Deep Reinforcement Learning Workshop (NIPS 2016)project page:&arxiv:&github:&
Learning to detect and localize many objects from few examples
Speed/accuracy trade-offs for modern convolutional object detectors
intro: Google Researcharxiv:&
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Feature Pyramid Networks for Object Detection
intro: Facebook AI Researcharxiv:&
Learning Object Class Detectors from Weakly Annotated Video
intro: CVPR 2012paper:
Analysing domain shift factors between videos and images for object detection
Video Object Recognition
Deep Learning for Saliency Prediction in Natural Video
intro: Submitted on 12 Jan 2016keywords: Deep learning, saliency map, optical flow, convolution network, contrast featurespaper:&
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Taskarxiv:&github:&
Object Detection from Video Tubelets with Convolutional Neural Networks
intro: CVPR 2016 Spotlight paperarxiv:&paper:&gihtub:&
Object Detection in Videos with Tubelets and Multi-context Cues
intro: SenseTime Groupslides:&slides:&
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
intro: BMVC 2016keywords: pseudo-labelerarxiv:&paper:&
CNN Based Object Detection in Large Video Images
intro: WangTao @ 爱奇艺keywords: object retrieval, object detection, scene classificationslides:&
YouTube-Objects dataset v2.2
homepage:&
ILSVRC2015: Object detection from video (VID)
homepage:&
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
This task involves predicting the salient regions of an image given by human eye fixations.
Best Deep Saliency Detection Models (CVPR 2016 & 2015)
Large-scale optimization of hierarchical features for saliency prediction in natural images
Predicting Eye Fixations using Convolutional Neural Networks
Saliency Detection by Multi-Context Deep Learning
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
Shallow and Deep Convolutional Networks for Saliency Prediction
arxiv:&github:&
Recurrent Attentional Networks for Saliency Detection
intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)arxiv:&
Two-Stream Convolutional Networks for Dynamic Saliency Prediction
Unconstrained Salient Object Detection
Unconstrained Salient Object Detection via Proposal Subset Optimization
intro: CVPR 2016project page:&paper:&github:&caffe model zoo:&
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Salient Object Subitizing
intro: CVPR 2015intro: predicting the existence and the number of salient objects in an image using holistic cuesproject page:&arxiv:&paper:&caffe model zoo:&
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)arxiv:&
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
intro: ECCV 2016arxiv:&
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
A Deep Multi-Level Network for Saliency Prediction
Visual Saliency Detection Based on Multiscale Deep CNN Features
intro: IEEE Transactions on Image Processingarxiv:&
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
intro: DSCLRCNarxiv:&
Deeply supervised salient object detection with short connections
Weakly Supervised Top-down Salient Object Detection
intro: Nanyang Technological Universityarxiv:&
Multi-view Face Detection Using Deep Convolutional Neural Networks
intro: Yahooarxiv:&
From Facial Parts Responses to Face Detection: A Deep Learning Approach
project page:&
Compact Convolutional Neural Network Cascade for Face Detection
arxiv:&github:&
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
intro: ECCV 2016arxiv:&github(MXNet):&
Supervised Transformer Network for Efficient Face Detection
UnitBox: An Advanced Object Detection Network
intro: ACM MM 2016arxiv:&
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 datasetarxiv:&
Grid Loss: Detecting Occluded Faces
intro: ECCV 2016arxiv:&paper:&poster:&
A Multi-Scale Cascade Fully Convolutional Network Face Detector
intro: ICPR 2016arxiv:&
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:&arxiv:&github(Matlab):&github(MXNet):&github:&
FDDB: Face Detection Data Set and Benchmark
homepage:&results:&
WIDER FACE: A Face Detection Benchmark
homepage:&arxiv:&
Deep Convolutional Network Cascade for Facial Point Detection
homepage:&paper:&github:&
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
intro: ECCV 2016arxiv:&
Detecting facial landmarks in the video based on a hybrid framework
Deep Constrained Local Models for Facial Landmark Detection
End-to-end people detection in crowded scenes
arxiv:&github:&ipn:
Detecting People in Artwork with CNNs
intro: ECCV 2016 Workshopsarxiv:&
Context-aware CNNs for person head detection
arxiv:&github:&
Pedestrian Detection aided by Deep Learning Semantic Tasks
intro: CVPR 2015project page:&paper:&
Deep Learning Strong Parts for Pedestrian Detection
intro: ICCV 2015. CUHK. DeepPartsintro: Achieving 11.89% average miss rate on Caltech Pedestrian Datasetpaper:&
Deep convolutional neural networks for pedestrian detection
arxiv:&github:&
New algorithm improves speed and accuracy of pedestrian detection
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:&
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
Is Faster R-CNN Doing Well for Pedestrian Detection?
arxiv:&github:&
Reduced Memory Region Based Deep Convolutional Neural Network Detection
intro: IEEE 2016 ICCE-Berlinarxiv:&
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
Multispectral Deep Neural Networks for Pedestrian Detection
intro: BMVC 2016 oralarxiv:&
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
intro: ECCV 2016arxiv:&
Traffic-Sign Detection and Classification in the Wild
project page(code+dataset):&paper:&code & model:&
Holistically-Nested Edge Detection
intro: ICCV 2015, Marr Prizepaper:&arxiv:&github:&
Unsupervised Learning of Edges
intro: CVPR 2016. Facebook AI Researcharxiv:&zn-blog:&
Pushing the Boundaries of Boundary Detection using Deep Learning
Convolutional Oriented Boundaries
intro: ECCV 2016arxiv:&
Richer Convolutional Features for Edge Detection
intro: richer convolutional features (RCF)arxiv:&
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
arxiv:&github:&
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
Deep Fruit Detection in Orchards
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
intro: The Journal of Field Robotics in May 2016project page:&arxiv:&
Deep Deformation Network for Object Landmark Localization
Fashion Landmark Detection in the Wild
Deep Learning for Fast and Accurate Fashion Item Detection
intro: Kuznech Inc.intro: MultiBox and Fast R-CNNpaper:
Visual Relationship Detection with Language Priors
intro: ECCV 2016 oralpaper:&github:&
OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)
Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
intro: IEEE SITIS 2016arxiv:&
Associative Embedding:End-to-End Learning for Joint Detection and Grouping
Deep Cuboid Detection: Beyond 2D Bounding Boxes
intro: CMU & Magic Leaparxiv:&
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
arxiv:&github:&
Scale-aware Pixel-wise Object Proposal Networks
intro: IEEE Transactions on Image Processingarxiv:&
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
intro: AttractioNetarxiv:&github:&
Learning to Segment Object Proposals via Recursive Neural Networks
Beyond Bounding Boxes: Precise Localization of Objects in Images
intro: PhD Thesishomepage:&phd-thesis:&github(“SDS using hypercolumns”):&
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Weakly Supervised Object Localization Using Size Estimates
Localizing objects using referring expressions
intro: ECCV 2016keywords: LSTM, multiple instance learning (MIL)paper:&github:&
LocNet: Improving Localization Accuracy for Object Detection
arxiv:&github:&
Learning Deep Features for Discriminative Localization
homepage:&arxiv:&github(Tensorflow):&github:&github:&
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
intro: ECCV 2016project page:&arxiv:&github:&
Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
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&&algorithm”github:&
Object detection in torch: Implementation of some object detection frameworks in torch
Using DIGITS to train an Object Detection network
FCN-MultiBox Detector
intro: Full convolution MultiBox Detector ( like SSD) implemented in Torch.github:&
Convolutional Neural Networks for Object Detection
Introducing automatic object detection to visual search (Pinterest)
keywords: Faster R-CNNblog:&demo:review:&
Deep Learning for Object Detection with DIGITS
Analyzing The Papers Behind Facebook’s Computer Vision Approach
keywords: DeepMask, SharpMask, MultiPathNetblog:&
**Easily Create High Quality Object Detectors with Deep Learning **
intro: dlib v19.2blog:&
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:&part 2:&
You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
part 1:&part 2:&
Faster R-CNN Pedestrian and Car Detection
blog:&ipn:&github:&
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