Object Detection

Oct 9, 2015

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)  
YOLO 63.4%   57.9%     45 fps
YOLO VGG-16 66.4%         21 fps
YOLOv2 544 × 544 78.6%   73.4%   21.6%(@[0.5-0.95]), 44.0%(@0.5) 40 fps
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

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

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

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

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

NoC

Object Detection Networks on Convolutional Feature Maps

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

DeepBox

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

YOLOv2

YOLO9000: Better, Faster, Stronger

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

R-CNN minus R

AttentionNet

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

https://github.com/weiliu89/caffe/issues/327

Enhancement of SSD by concatenating feature maps for object detection

DSSD

DSSD : Deconvolutional Single Shot Detector

Context-aware Single-Shot Detector

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

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

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

MultiPathNet

A MultiPath Network for Object Detection

CRAFT

CRAFT Objects from Images

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

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

R-FCN

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

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: Deep but Lightweight Neural Networks for Real-time Object Detection

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

StuffNet

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

CC-Net

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

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

https://arxiv.org/abs/1703.10295

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

https://arxiv.org/abs/1704.05775

S-OHEM: Stratified Online Hard Example Mining for Object Detection

https://arxiv.org/abs/1705.02233

LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

Point Linking Network for Object Detection

Perceptual Generative Adversarial Networks for Small Object Detection

https://arxiv.org/abs/1706.05274

Few-shot Object Detection

https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

https://arxiv.org/abs/1706.09180

SMC Faster R-CNN: Toward a scene-specialized multi-object detector

https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

https://arxiv.org/abs/1707.01395

RON: Reverse Connection with Objectness Prior Networks for Object Detection

Residual Features and Unified Prediction Network for Single Stage Detection

https://arxiv.org/abs/1707.05031

Deformable Part-based Fully Convolutional Network for Object Detection

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

Focal Loss for Dense Object Detection

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

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

Learning non-maximum suppression

https://arxiv.org/abs/1705.02950

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

Detection From Video

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

T-CNN

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

http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

Object Detection in 3D

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

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

https://arxiv.org/abs/1703.03347

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)

http://i.cs.hku.hk/~yzyu/vision.html

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

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

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

https://arxiv.org/abs/1703.00152

Supervised Adversarial Networks for Image Saliency Detection

https://arxiv.org/abs/1704.07242

Group-wise Deep Co-saliency Detection

https://arxiv.org/abs/1707.07381

Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

Learning Uncertain Convolutional Features for Accurate Saliency Detection

Saliency Detection in Video

Deep Learning For Video Saliency Detection

Video Salient Object Detection Using Spatiotemporal Deep Features

https://arxiv.org/abs/1708.01447

Visual Relationship Detection

Visual Relationship Detection with Language Priors

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

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

Identifying Spatial Relations in Images using Convolutional Neural Networks

https://arxiv.org/abs/1706.04215

PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN

Specific Object Deteciton

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

Finding Tiny Faces

Towards a Deep Learning Framework for Unconstrained Face Detection

Supervised Transformer Network for Efficient Face Detection

UnitBox

UnitBox: An Advanced Object Detection Network

Bootstrapping Face Detection with Hard Negative Examples

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

https://arxiv.org/abs/1703.10818

Face R-CNN

https://arxiv.org/abs/1706.01061

Face Detection through Scale-Friendly Deep Convolutional Networks

https://arxiv.org/abs/1706.02863

Scale-Aware Face Detection

Multi-Branch Fully Convolutional Network for Face Detection

https://arxiv.org/abs/1707.06330

SSH: Single Stage Headless Face Detector

Facial Point / Landmark Detection

Deep Convolutional Network Cascade for Facial Point Detection

Facial Landmark Detection by Deep Multi-task Learning

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

Detecting facial landmarks in the video based on a hybrid framework

Deep Constrained Local Models for Facial Landmark Detection

Effective face landmark localization via single deep network

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

https://arxiv.org/abs/1704.01880

Deep Alignment Network: A convolutional neural network for robust face alignment

People Detection

End-to-end people detection in crowded scenes

Detecting People in Artwork with CNNs

Deep Multi-camera People Detection

Person Head Detection

Context-aware CNNs for person head detection

Pedestrian Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

Deep Learning Strong Parts for Pedestrian Detection

Taking a Deeper Look at Pedestrians

Convolutional Channel Features

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%”
  • arxiv: http://arxiv.org/abs/1603.04525

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?

Reduced Memory Region Based Deep Convolutional Neural Network Detection

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

Multispectral Deep Neural Networks for Pedestrian Detection

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

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

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

Evolving Boxes for fast Vehicle Detection

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

Detecting Small Signs from Large Images

Boundary / Edge / Contour Detection

Holistically-Nested Edge Detection

Unsupervised Learning of Edges

Pushing the Boundaries of Boundary Detection using Deep Learning

Convolutional Oriented Boundaries

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

Richer Convolutional Features for Edge Detection

Contour Detection from Deep Patch-level Boundary Prediction

https://arxiv.org/abs/1705.03159

CASENet: Deep Category-Aware Semantic Edge 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

Fruit Detection

Deep Fruit Detection in Orchards

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Part Detection

Objects as context for part detection

https://arxiv.org/abs/1703.09529

Others

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

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

Automatic Handgun Detection Alarm in Videos Using Deep Learning

Using Deep Networks for Drone Detection

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance 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)
  • arxiv: https://arxiv.org/abs/1704.03533

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

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

https://arxiv.org/abs/1705.10034

Tutorials / Talks

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

Towards Good Practices for Recognition & Detection

Projects

TensorBox: a simple framework for training neural networks to detect objects in images

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

Using DIGITS to train an Object Detection network

FCN-MultiBox Detector

KittiBox: A car detection model implemented in Tensorflow.

Deformable Convolutional Networks + MST + Soft-NMS

Tools

BeaverDam: Video annotation tool for deep learning training labels

https://github.com/antingshen/BeaverDam

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-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