目標檢測領域論文和程式碼集合(2013年~2018年8月)
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Method | backbone | test size | 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% | 31.84% | |||||
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 | ResNet101 | 85.6% | 83.8% | 37.4%(@[0.5-0.95]), 59.0%(@0.5) | ||||
YOLO | 63.4% | 57.9% | 45 fps | |||||
YOLO VGG-16 | 66.4% | 21 fps | ||||||
YOLOv2 | 448x448 | 78.6% | 73.4% | 21.6%(@[0.5-0.95]), 44.0%(@0.5) | 40 fps | |||
SSD | VGG16 | 300x300 | 77.2% | 75.8% | 25.1%(@[0.5-0.95]), 43.1%(@0.5) | 46 fps | ||
SSD | VGG16 | 512x512 | 79.8% | 78.5% | 28.8%(@[0.5-0.95]), 48.5%(@0.5) | 19 fps | ||
SSD | ResNet101 | 300x300 | 28.0%(@[0.5-0.95]) | 16 fps | ||||
SSD | ResNet101 | 512x512 | 31.2%(@[0.5-0.95]) | 8 fps | ||||
DSSD | ResNet101 | 300x300 | 28.0%(@[0.5-0.95]) | 8 fps | ||||
DSSD | ResNet101 | 500x500 | 33.2%(@[0.5-0.95]) | 6 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 | ResNet50 | 77.4% | 0.12sec(K40), 0.09sec(TitianX) | |||||
R-FCN | ResNet101 | 79.5% | 0.17sec(K40), 0.12sec(TitianX) | |||||
R-FCN(ms train) | ResNet101 | 83.6% | 82.0% | 31.5%(@[0.5-0.95]), 53.2%(@0.5) | ||||
PVANet 9.0 | 84.9% | 84.2% | 750ms(CPU), 46ms(TitianX) | |||||
RetinaNet | ResNet101-FPN | |||||||
Light-Head R-CNN | Xception* | 800/1200 | 31.5%@[0.5:0.95] | 95 fps | ||||
Light-Head R-CNN | Xception* | 700/1100 | 30.7%@[0.5:0.95] | 102 fps |
Papers
Deep Neural Networks for Object Detection
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
R-CNN
Rich feature hierarchies for accurate object detection and semantic segmentation
Fast R-CNN
Fast R-CNN
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Faster R-CNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
R-CNN minus R
Faster R-CNN in MXNet with distributed implementation and data parallelization
Contextual Priming and Feedback for Faster R-CNN
An Implementation of Faster RCNN with Study for Region Sampling
Interpretable R-CNN
- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
Light-Head R-CNN
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Cascade R-CNN
Cascade R-CNN: Delving into High Quality Object Detection
MultiBox
Scalable Object Detection using Deep Neural Networks
Scalable, High-Quality Object Detection
SPP-Net
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
Object Detectors Emerge in Deep Scene CNNs
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Object Detection Networks on Convolutional Feature Maps
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
DeepBox: Learning Objectness with Convolutional Networks
MR-CNN
Object detection via a multi-region & semantic segmentation-aware CNN model
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
YOLO: Core ML versus MPSNNGraph
TensorFlow YOLO object detection on Android
- intro: Real-time object detection on Android using the YOLO network with TensorFlow
Computer Vision in iOS – Object Detection
YOLOv2
YOLO9000: Better, Faster, Stronger
darknet_scripts
- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
LightNet: Bringing pjreddie’s DarkNet out of the shadows
YOLO v2 Bounding Box Tool
- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
YOLOv3
YOLOv3: An Incremental Improvement
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
DenseBox
DenseBox: Unifying Landmark Localization with End to End Object Detection
SSD
SSD: Single Shot MultiBox Detector
What’s the diffience in performance between this new code you pushed and the previous code? #327
DSSD
DSSD : Deconvolutional Single Shot Detector
Enhancement of SSD by concatenating feature maps for object detection
Context-aware Single-Shot Detector
- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
Feature-Fused SSD: Fast Detection for Small Objects
FSSD
FSSD: Feature Fusion Single Shot Multibox Detector
Weaving Multi-scale Context for Single Shot Detector
- intro: WeaveNet
- keywords: fuse multi-scale information
ESSD
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
Inside-Outside Net (ION)
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Adaptive Object Detection Using Adjacency and Zoom Prediction
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
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
CRAFT
CRAFT Objects from Images
OHEM
Training Region-based Object Detectors with Online Hard Example Mining
S-OHEM: Stratified Online Hard Example Mining for Object Detection
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
- intro: CVPR 2016
- keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Recycle deep features for better object detection
MS-CNN
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Multi-stage Object Detection with Group Recursive Learning
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
PVANET
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
GBD-Net
Gated Bi-directional CNN for Object Detection
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)
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
Learning to detect and localize many objects from few examples
Speed/accuracy trade-offs for modern convolutional object detectors
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Feature Pyramid Network (FPN)
Feature Pyramid Networks for Object Detection
Action-Driven Object Detection with Top-Down Visual Attentions
Beyond Skip Connections: Top-Down Modulation for Object Detection
Wide-Residual-Inception Networks for Real-time Object Detection
Attentional Network for Visual Object Detection
- intro: University of Maryland & Mitsubishi Electric Research Laboratories
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
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
Spatial Memory for Context Reasoning in Object Detection
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
- intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
Point Linking Network for Object Detection
Perceptual Generative Adversarial Networks for Small Object Detection
Few-shot Object Detection
Yes-Net: An effective Detector Based on Global Information
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Towards lightweight convolutional neural networks for object detection
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Mimicking Very Efficient Network for Object Detection
Residual Features and Unified Prediction Network for Single Stage Detection
Deformable Part-based Fully Convolutional Network for Object Detection
- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Recurrent Scale Approximation for Object Detection in CNN
DSOD
DSOD: Learning Deeply Supervised Object Detectors from Scratch
RetinaNet
Focal Loss for Dense Object Detection
- intro: ICCV 2017 Best student paper award. Facebook AI Research
- keywords: RetinaNet
Focal Loss Dense Detector for Vehicle Surveillance
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
Incremental Learning of Object Detectors without Catastrophic Forgetting
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Zero-Annotation Object Detection with Web Knowledge Transfer
- intro: NTU, Singapore & Amazon
- keywords: multi-instance multi-label domain adaption learning framework
MegDet
MegDet: A Large Mini-Batch Object Detector
- intro: Peking University & Tsinghua University & Megvii Inc
Single-Shot Refinement Neural Network for Object Detection
Receptive Field Block Net for Accurate and Fast Object Detection
An Analysis of Scale Invariance in Object Detection - SNIP
Feature Selective Networks for Object Detection
Learning a Rotation Invariant Detector with Rotatable Bounding Box
Scalable Object Detection for Stylized Objects
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Deep Regionlets for Object Detection
- keywords: region selection network, gating network
Training and Testing Object Detectors with Virtual Images
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
Spot the Difference by Object Detection
Localization-Aware Active Learning for Object Detection
Object Detection with Mask-based Feature Encoding
LSTD: A Low-Shot Transfer Detector for Object Detection
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Pseudo Mask Augmented Object Detection
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Learning Region Features for Object Detection
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
- intro: Singapore Management University & Zhejiang University
Object Detection for Comics using Manga109 Annotations
- intro: University of Tokyo & National Institute of Informatics, Japan
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Transferring Common-Sense Knowledge for Object Detection
Multi-scale Location-aware Kernel Representation for Object Detection
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- intro: National University of Defense Technology
DetNet: A Backbone network for Object Detection
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
AdvDetPatch: Attacking Object Detectors with Adversarial Patches
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Object detection at 200 Frames Per Second
- intro: United Technologies Research Center-Ireland
Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
SNIPER: Efficient Multi-Scale Training
Soft Sampling for Robust Object Detection
Non-Maximum Suppression (NMS)
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
A convnet for non-maximum suppression
Improving Object Detection With One Line of Code
Soft-NMS – Improving Object Detection With One Line of Code
Learning non-maximum suppression
Relation Networks for Object Detection
Adversarial Examples
Adversarial Examples that Fool Detectors
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Weakly Supervised Object Detection
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
Weakly supervised object detection using pseudo-strong labels
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
Video Object Detection
Learning Object Class Detectors from Weakly Annotated Video
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 2016
- keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Object Detection from Video Tubelets with Convolutional Neural Networks
Object Detection in Videos with Tubelets and Multi-context Cues
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
CNN Based Object Detection in Large Video Images
Object Detection in Videos with Tubelet Proposal Networks
Flow-Guided Feature Aggregation for Video Object Detection
Video Object Detection using Faster R-CNN
Improving Context Modeling for Video Object Detection and Tracking
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
Mobile Video Object Detection with Temporally-Aware Feature Maps
Towards High Performance Video Object Detection
Impression Network for Video Object Detection
Spatial-Temporal Memory Networks for Video Object Detection
3D-DETNet: a Single Stage Video-Based Vehicle Detector
Object Detection in Videos by Short and Long Range Object Linking
Object Detection in Video with Spatiotemporal Sampling Networks
- intro: University of Pennsylvania, 2Dartmouth College
Towards High Performance Video Object Detection for Mobiles
Optimizing Video Object Detection via a Scale-Time Lattice
Object Detection on Mobile Devices
Pelee: A Real-Time Object Detection System on Mobile Devices
Object Detection in 3D
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
- intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
Object Detection on RGB-D
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Differential Geometry Boosts Convolutional Neural Networks for Object Detection
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
Zero-Shot Object Detection
Zero-Shot Detection
- intro: Australian National University
- keywords: YOLO
Zero-Shot Object Detection
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Zero-Shot Object Detection by Hybrid Region Embedding
- intro: Middle East Technical University & Hacettepe University
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)
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
Recurrent Attentional Networks for Saliency Detection
- intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
Two-Stream Convolutional Networks for Dynamic Saliency Prediction
Unconstrained Salient Object Detection
Unconstrained Salient Object Detection via Proposal Subset Optimization
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Salient Object Subitizing
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
- intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
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
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
Deeply supervised salient object detection with short connections
Weakly Supervised Top-down Salient Object Detection
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
Saliency Detection by Forward and Backward Cues in Deep-CNNs
Supervised Adversarial Networks for Image Saliency Detection
Group-wise Deep Co-saliency Detection
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
- intro: University of Maryland College Park & eBay Inc
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Learning Uncertain Convolutional Features for Accurate Saliency Detection
Deep Edge-Aware Saliency Detection
Self-explanatory Deep Salient Object Detection
- intro: National University of Defense Technology, China & National University of Singapore
PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
Recurrently Aggregating Deep Features for Salient Object Detection
Deep saliency: What is learnt by a deep network about saliency?
- intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
Contrast-Oriented Deep Neural Networks for Salient Object Detection
Salient Object Detection by Lossless Feature Reflection
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
Video Saliency Detection
Deep Learning For Video Saliency Detection
Video Salient Object Detection Using Spatiotemporal Deep Features
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
Visual Relationship Detection
Visual Relationship Detection with Language Priors
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)
Visual Translation Embedding Network for Visual Relation Detection
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
Detecting Visual Relationships with Deep Relational Networks
- intro: CVPR 2017 oral. The Chinese University of Hong Kong
Identifying Spatial Relations in Images using Convolutional Neural Networks
PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
Natural Language Guided Visual Relationship Detection
Face Deteciton
Multi-view Face Detection Using Deep Convolutional Neural Networks
From Facial Parts Responses to Face Detection: A Deep Learning Approach
Compact Convolutional Neural Network Cascade for Face Detection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Towards a Deep Learning Framework for Unconstrained Face Detection
Supervised Transformer Network for Efficient Face Detection
UnitBox: An Advanced Object Detection Network
Bootstrapping Face Detection with Hard Negative Examples
- author: 萬韶華 @ 小米.
- intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
Grid Loss: Detecting Occluded Faces
A Multi-Scale Cascade Fully Convolutional Network Face Detector
MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
Face Detection using Deep Learning: An Improved Faster RCNN Approach
Faceness-Net: Face Detection through Deep Facial Part Responses
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”
End-To-End Face Detection and Recognition
Face R-CNN
Face Detection through Scale-Friendly Deep Convolutional Networks
Scale-Aware Face Detection
- intro: CVPR 2017. SenseTime & Tsinghua University
Detecting Faces Using Inside Cascaded Contextual CNN
Multi-Branch Fully Convolutional Network for Face Detection
SSH: Single Stage Headless Face Detector
Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
- intro: IJCB 2017
- keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
- intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
S3FD: Single Shot Scale-invariant Face Detector
Detecting Faces Using Region-based Fully Convolutional Networks
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
Face Attention Network: An effective Face Detector for the Occluded Faces
Feature Agglomeration Networks for Single Stage Face Detection
Face Detection Using Improved Faster RCNN
PyramidBox: A Context-assisted Single Shot Face Detector
A Fast Face Detection Method via Convolutional Neural Network
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
- intro: CVPR 2018. Beihang University & CUHK & Sensetime
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
SFace: An Efficient Network for Face Detection in Large Scale Variations
- intro: Beihang University & Megvii Inc. (Face++)
Survey of Face Detection on Low-quality Images
Anchor Cascade for Efficient Face Detection
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
Detect Small Faces
Finding Tiny Faces
Detecting and counting tiny faces
- intro: ENS Paris-Saclay. ExtendedTinyFaces
- intro: Detecting and counting small objects - Analysis, review and application to counting
Seeing Small Faces from Robust Anchor’s Perspective
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
Person Head Detection
Context-aware CNNs for person head detection
Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture
Pedestrian Detection / People Detection
Pedestrian Detection aided by Deep Learning Semantic Tasks
Deep Learning Strong Parts for Pedestrian Detection
- intro: ICCV 2015. CUHK. DeepParts
- intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
Taking a Deeper Look at Pedestrians
Convolutional Channel Features
End-to-end people detection in crowded scenes
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Deep convolutional neural networks for pedestrian detection
Scale-aware Fast R-CNN for Pedestrian Detection
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%”
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?
Unsupervised Deep Domain Adaptation for Pedestrian Detection
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
Detecting People in Artwork with CNNs
Multispectral Deep Neural Networks for Pedestrian Detection
Deep Multi-camera People Detection
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
What Can Help Pedestrian Detection?
Illuminating Pedestrians via Simultaneous Detection & Segmentation
[https://arxiv.org/abs/1706.08564](https://arxiv.org/abs/1706.08564
Rotational Rectification Network for Robust Pedestrian Detection
STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos
- intro: The University of North Carolina at Chapel Hill
Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy
Repulsion Loss: Detecting Pedestrians in a Crowd
Aggregated Channels Network for Real-Time Pedestrian Detection
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
- intro: State Key Lab of CAD&CG, Zhejiang University
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
PCN: Part and Context Information for Pedestrian Detection with CNNs
- intro: British Machine Vision Conference(BMVC) 2017
Vehicle Detection
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
Evolving Boxes for fast Vehicle Detection
Fine-Grained Car Detection for Visual Census Estimation
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
- intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
Traffic-Sign Detection
Traffic-Sign Detection and Classification in the Wild
Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
Detecting Small Signs from Large Images
- intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
Detecting Traffic Lights by Single Shot Detection
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
Skeleton Detection
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
Fruit Detection
Deep Fruit Detection in Orchards
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
Shadow Detection
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
Direction-aware Spatial Context Features for Shadow Detection
Direction-aware Spatial Context Features for Shadow Detection and Removal
- intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
Others Detection
Deep Deformation Network for Object Landmark Localization
Fashion Landmark Detection in the Wild
Deep Learning for Fast and Accurate Fashion Item Detection
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
Associative Embedding:End-to-End Learning for Joint Detection and Grouping
Deep Cuboid Detection: Beyond 2D Bounding Boxes
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Deep Learning Logo Detection with Data Expansion by Synthesising Context
Scalable Deep Learning Logo Detection
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
Automatic Handgun Detection Alarm in Videos Using Deep Learning
Objects as context for part detection
Using Deep Networks for Drone Detection
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
Target Driven Instance Detection
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
Deep Learning Object Detection Methods for Ecological Camera Trap Data
- intro: Conference of Computer and Robot Vision. University of Guelph
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Object Proposal
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
Scale-aware Pixel-wise Object Proposal Networks
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
Learning to Segment Object Proposals via Recursive Neural Networks
Learning Detection with Diverse Proposals
- intro: CVPR 2017
- keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
Improving Small Object Proposals for Company Logo Detection
Localization
Beyond Bounding Boxes: Precise Localization of Objects in Images
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Weakly Supervised Object Localization Using Size Estimates
Active Object Localization with Deep Reinforcement Learning
- intro: ICCV 2015
- keywords: Markov Decision Process
Localizing objects using referring expressions
LocNet: Improving Localization Accuracy for Object Detection
Learning Deep Features for Discriminative Localization
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
Ensemble of Part Detectors for Simultaneous Classification and Localization
STNet: Selective Tuning of Convolutional Networks for Object Localization
Soft Proposal Networks for Weakly Supervised Object Localization
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Tutorials / Talks
Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
Towards Good Practices for Recognition & Detection
Work in progress: Improving object detection and instance segmentation for small objects
Projects
Detectron
- intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
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”
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.
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
Deformable Convolutional Networks + MST + Soft-NMS
How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow
Leaderboard
Detection Results: VOC2012
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)
Deep Learning for Object Detection with DIGITS
Analyzing The Papers Behind Facebook’s Computer Vision Approach
Easily Create High Quality Object Detectors with Deep Learning
How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
Object Detection in Satellite Imagery, a Low Overhead Approach
You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
Faster R-CNN Pedestrian and Car Detection
Small U-Net for vehicle detection
Region of interest pooling explained
Supercharge your Computer Vision models with the TensorFlow Object Detection API
Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning
One-shot object detection