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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