转：awesome-object-detection

0001，

object-detection

[TOC]

This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to Date.

  • R-CNN
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN
  • Light-Head R-CNN
  • Cascade R-CNN
  • SPP-Net
  • YOLO
  • YOLOv2
  • YOLOv3
  • YOLT
  • SSD
  • DSSD
  • FSSD
  • ESSD
  • MDSSD
  • Pelee
  • Fire SSD
  • R-FCN
  • FPN
  • DSOD
  • RetinaNet
  • MegDet
  • RefineNet
  • DetNet
  • SSOD
  • CornerNet
  • M2Det
  • 3D Object Detection
  • ZSD（Zero-Shot Object Detection）
  • OSD（One-Shot object Detection）
  • Weakly Supervised Object Detection
  • Softer-NMS
  • 2018
  • 2019
  • Other

Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

 

Survey

《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》

《Deep Learning for Generic Object Detection: A Survey》

 

Papers&Codes

 

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

Domain Adaptive Faster R-CNN for Object Detection in the Wild

 

Mask R-CNN

 

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

 

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

 

YOLO

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

img

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

img

YOLO: Core ML versus MPSNNGraph

TensorFlow YOLO object detection on Android

Computer Vision in iOS – Object Detection

 

YOLOv2

YOLO9000: Better, Faster, Stronger

darknet_scripts

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

LightNet: Bringing pjreddie's DarkNet out of the shadows

https://github.com//explosion/lightnet

YOLO v2 Bounding Box Tool

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

  • intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
  • arxiv: https://arxiv.org/abs/1804.04606

Object detection at 200 Frames Per Second

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

OmniDetector: With Neural Networks to Bounding Boxes

 

YOLOv3

YOLOv3: An Incremental Improvement

 

YOLT

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

 

SSD

SSD: Single Shot MultiBox Detector

img

What's the diffience in performance between this new code you pushed and the previous code? #327

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

 

DSSD

DSSD : Deconvolutional Single Shot Detector

Enhancement of SSD by concatenating feature maps for object detection

Context-aware Single-Shot Detector

Feature-Fused SSD: Fast Detection for Small Objects

https://arxiv.org/abs/1709.05054

 

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

 

ESSD

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

https://arxiv.org/abs/1801.05918

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

https://arxiv.org/abs/1802.06488

 

MDSSD

MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

 

Pelee

Pelee: A Real-Time Object Detection System on Mobile Devices

https://github.com/Robert-JunWang/Pelee

 

Fire SSD

Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

 

R-FCN

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

R-FCN-3000 at 30fps: Decoupling Detection and Classification

https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

 

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

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

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

https://arxiv.org/abs/1704.05775

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

Mimicking Very Efficient Network 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

img

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

Object Detection from Scratch with Deep Supervision

 

RetinaNet

Focal Loss for Dense Object Detection

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

https://arxiv.org/abs/1709.04347

StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

https://arxiv.org/abs/1709.05788

Dynamic Zoom-in Network for Fast Object Detection in Large Images

https://arxiv.org/abs/1711.05187

Zero-Annotation Object Detection with Web Knowledge Transfer

 

MegDet

MegDet: A Large Mini-Batch Object Detector

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

https://arxiv.org/abs/1711.08879

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

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
  • arxiv: https://arxiv.org/abs/1712.08832

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

Pseudo Mask Augmented Object Detection

https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://arxiv.org/abs/1803.06799

Learning Region Features for Object Detection

Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

Object Detection for Comics using Manga109 Annotations

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

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

 

RefineNet

Single-Shot Refinement Neural Network for Object Detection

 

DetNet

DetNet: A Backbone network for Object Detection

 

SSOD

Self-supervisory Signals for Object Discovery and Detection

 

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

 

M2Det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

 

3D Object Detection

3D Backbone Network for 3D Object Detection

LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs

 

ZSD

Zero-Shot Detection

Zero-Shot Object Detection

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

Zero-Shot Object Detection by Hybrid Region Embedding

 

OSD

One-Shot Object Detection

RepMet: Representative-based metric learning for classification and one-shot object detection

 

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

 

Softer-NMS

《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》

 

2019

Object Detection based on Region Decomposition and Assembly

Bottom-up Object Detection by Grouping Extreme and Center Points

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

Consistent Optimization for Single-Shot Object Detection

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

Region Proposal by Guided Anchoring

Scale-Aware Trident Networks for Object Detection

 

2018

Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions

Strong-Weak Distribution Alignment for Adaptive Object Detection

AutoFocus: Efficient Multi-Scale Inference

  • intro: AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU
  • arXiv: https://arxiv.org/abs/1812.01600

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

Grid R-CNN

Deformable ConvNets v2: More Deformable, Better Results

Anchor Box Optimization for Object Detection

Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

Gradient Harmonized Single-stage Detector

CFENet: Object Detection with Comprehensive Feature Enhancement Module

DeRPN: Taking a further step toward more general object detection

Hybrid Knowledge Routed Modules for Large-scale Object Detection

《Receptive Field Block Net for Accurate and Fast Object Detection》

Deep Feature Pyramid Reconfiguration for Object Detection

Unsupervised Hard Example Mining from Videos for Improved Object Detection

Acquisition of Localization Confidence for Accurate Object Detection

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

MetaAnchor: Learning to Detect Objects with Customized Anchors

Relation Network for Object Detection

Quantization Mimic: Towards Very Tiny CNN for Object Detection

Learning Rich Features for Image Manipulation Detection

SNIPER: Efficient Multi-Scale Training

Soft Sampling for Robust Object Detection

Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria

 

Other

R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

 

Detection Toolbox

  • Detectron(FAIR): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

  • maskrcnn-benchmark(FAIR): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

  • mmdetection(SenseTime&CUHK): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.

 

0002，

deep learning object detection

A paper list of object detection using deep learning. I worte this page with reference to this survey paper and searching and searching..

Last updated: 2019/03/18

 

Update log

2018/9/18 - update all of recent papers and make some diagram about history of object detection using deep learning. 2018/9/26 - update codes of papers. (official and unofficial)
2018/october - update 5 papers and performance table.
2018/november - update 9 papers.
2018/december - update 8 papers and and performance table and add new diagram(2019 version!!).
2019/january - update 4 papers and and add commonly used datasets.
2019/february - update 3 papers.
2019/march - update figure and code links.

 

Table of Contents

 

Paper list from 2014 to now(2019)

The part highlighted with red characters means papers that i think "must-read". However, it is my personal opinion and other papers are important too, so I recommend to read them if you have time.

 

Performance table

FPS(Speed) index is related to the hardware spec(e.g. CPU, GPU, RAM, etc), so it is hard to make an equal comparison. The solution is to measure the performance of all models on hardware with equivalent specifications, but it is very difficult and time consuming.

DetectorVOC07 (mAP@IoU=0.5)VOC12 (mAP@IoU=0.5)COCO (mAP@IoU=0.5:0.95)Published In
R-CNN 58.5 - - CVPR'14
SPP-Net 59.2 - - ECCV'14
MR-CNN 78.2 (07+12) 73.9 (07+12) - ICCV'15
Fast R-CNN 70.0 (07+12) 68.4 (07++12) 19.7 ICCV'15
Faster R-CNN 73.2 (07+12) 70.4 (07++12) 21.9 NIPS'15
YOLO v1 66.4 (07+12) 57.9 (07++12) - CVPR'16
G-CNN 66.8 66.4 (07+12) - CVPR'16
AZNet 70.4 - 22.3 CVPR'16
ION 80.1 77.9 33.1 CVPR'16
HyperNet 76.3 (07+12) 71.4 (07++12) - CVPR'16
OHEM 78.9 (07+12) 76.3 (07++12) 22.4 CVPR'16
MPN - - 33.2 BMVC'16
SSD 76.8 (07+12) 74.9 (07++12) 31.2 ECCV'16
GBDNet 77.2 (07+12) - 27.0 ECCV'16
CPF 76.4 (07+12) 72.6 (07++12) - ECCV'16
R-FCN 79.5 (07+12) 77.6 (07++12) 29.9 NIPS'16
DeepID-Net 69.0 - - PAMI'16
NoC 71.6 (07+12) 68.8 (07+12) 27.2 TPAMI'16
DSSD 81.5 (07+12) 80.0 (07++12) 33.2 arXiv'17
TDM - - 37.3 CVPR'17
FPN - - 36.2 CVPR'17
YOLO v2 78.6 (07+12) 73.4 (07++12) - CVPR'17
RON 77.6 (07+12) 75.4 (07++12) 27.4 CVPR'17
DeNet 77.1 (07+12) 73.9 (07++12) 33.8 ICCV'17
CoupleNet 82.7 (07+12) 80.4 (07++12) 34.4 ICCV'17
RetinaNet - - 39.1 ICCV'17
DSOD 77.7 (07+12) 76.3 (07++12) - ICCV'17
SMN 70.0 - - ICCV'17
Light-Head R-CNN - - 41.5 arXiv'17
YOLO v3 - - 33.0 arXiv'18
SIN 76.0 (07+12) 73.1 (07++12) 23.2 CVPR'18
STDN 80.9 (07+12) - - CVPR'18
RefineDet 83.8 (07+12) 83.5 (07++12) 41.8 CVPR'18
SNIP - - 45.7 CVPR'18
Relation-Network - - 32.5 CVPR'18
Cascade R-CNN - - 42.8 CVPR'18
MLKP 80.6 (07+12) 77.2 (07++12) 28.6 CVPR'18
Fitness-NMS - - 41.8 CVPR'18
RFBNet 82.2 (07+12) - - ECCV'18
CornerNet - - 42.1 ECCV'18
PFPNet 84.1 (07+12) 83.7 (07++12) 39.4 ECCV'18
Pelee 70.9 (07+12) - - NIPS'18
HKRM 78.8 (07+12) - 37.8 NIPS'18
M2Det - - 44.2 AAAI'19
R-DAD 81.2 (07++12) 82.0 (07++12) 43.1 AAAI'19

 

2014

 

2015

 

2016

  • [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR' 16] |[pdf] [official code - c]

  • [G-CNN] G-CNN: an Iterative Grid Based Object Detector | Mahyar Najibi, et al. | [CVPR' 16] |[pdf]

  • [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | Yongxi Lu, Tara Javidi. | [CVPR' 16] |[pdf]

  • [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | Sean Bell, et al. | [CVPR' 16] |[pdf]

  • [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | Tao Kong, et al. | [CVPR' 16] |[pdf]

  • [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR' 16] |[pdf] [official code - caffe]

  • [CRAPF] CRAFT Objects from Images | Bin Yang, et al. | [CVPR' 16] |[pdf] [official code - caffe]

  • [MPN] A MultiPath Network for Object Detection | Sergey Zagoruyko, et al. | [BMVC' 16] |[pdf] [official code - torch]

  • [SSD] SSD: Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV' 16] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch]

  • [GBDNet] Crafting GBD-Net for Object Detection | Xingyu Zeng, et al. | [ECCV' 16] |[pdf] [official code - caffe]

  • [CPF] Contextual Priming and Feedback for Faster R-CNN | Abhinav Shrivastava and Abhinav Gupta | [ECCV' 16] |[pdf]

  • [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | Zhaowei Cai, et al. | [ECCV' 16] |[pdf] [official code - caffe]

  • [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS' 16] |[pdf] [official code - caffe] [unofficial code - caffe]

  • [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | Kye-Hyeon Kim, et al. | [NIPSW' 16] |[pdf] [official code - caffe]

  • [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | Wanli Ouyang, et al. | [PAMI' 16] |[pdf]

  • [NoC] Object Detection Networks on Convolutional Feature Maps | Shaoqing Ren, et al. | [TPAMI' 16] |[pdf]

 

2017

 

2018

  • [YOLO v3] YOLOv3: An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv' 18] |[pdf] [official code - c] [unofficial code - pytorch] [unofficial code - pytorch] [unofficial code - keras] [unofficial code - tensorflow]

  • [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | Hongyang Li, et al. | [IJCV' 18] |[pdf] [official code - caffe]

  • [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | Yong Liu, et al. | [CVPR' 18] |[pdf] [official code - tensorflow]

  • [STDN] Scale-Transferrable Object Detection | Peng Zhou, et al. | [CVPR' 18] |[pdf]

  • [RefineDet] Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR' 18] |[pdf] [official code - caffe] [unofficial code - chainer] [unofficial code - pytorch]

  • [MegDet] MegDet: A Large Mini-Batch Object Detector | Chao Peng, et al. | [CVPR' 18] |[pdf]

  • [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | Yuhua Chen, et al. | [CVPR' 18] |[pdf] [official code - caffe]

  • [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | Bharat Singh, Larry S. Davis | [CVPR' 18] |[pdf]

  • [Relation-Network] Relation Networks for Object Detection | Han Hu, et al. | [CVPR' 18] |[pdf] [official code - mxnet]

  • [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | Zhaowei Cai, et al. | [CVPR' 18] |[pdf] [official code - caffe]

  • Finding Tiny Faces in the Wild with Generative Adversarial Network | Yancheng Bai, et al. | [CVPR' 18] |[pdf]

  • [MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | Hao Wang, et al. | [CVPR' 18] |[pdf] [official code - caffe]

  • Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | Naoto Inoue, et al. | [CVPR' 18] |[pdf] [official code - chainer]

  • [Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | Lachlan Tychsen-Smith, Lars Petersson. | [CVPR' 18] |[pdf]

  • [STDnet] STDnet: A ConvNet for Small Target Detection | Brais Bosquet, et al. | [BMVC' 18] |[pdf]

  • [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | Songtao Liu, et al. | [ECCV' 18] |[pdf] [official code - pytorch]

  • Zero-Annotation Object Detection with Web Knowledge Transfer | Qingyi Tao, et al. | [ECCV' 18] |[pdf]

  • [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | Hei Law, et al. | [ECCV' 18] |[pdf] [official code - pytorch]

  • [PFPNet] Parallel Feature Pyramid Network for Object Detection | Seung-Wook Kim, et al. | [ECCV' 18] |[pdf]

  • [Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | Yihui He, et al. | [arXiv' 18] |[pdf]

  • [ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | Shang-Tse Chen, et al. | [ECML-PKDD' 18] |[pdf] [official code - tensorflow]

  • [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | Jun Wang, et al. | [NIPS' 18] |[pdf] [official code - caffe]

  • [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | ChenHan Jiang, et al. | [NIPS' 18] |[pdf]

  • [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | Tong Yang, et al. | [NIPS' 18] |[pdf]

  • [SNIPER] SNIPER: Efficient Multi-Scale Training | Bharat Singh, et al. | [NIPS' 18] |[pdf]

 

2019

  • [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Qijie Zhao, et al. | [AAAI' 19] |[pdf] [official code - pytorch]

  • [R-DAD] Object Detection based on Region Decomposition and Assembly | Seung-Hwan Bae | [AAAI' 19] |[pdf]

  • [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | Yang Zhang, et al. | [ICLR' 19] |[pdf]

 

Dataset Papers

Statistics of commonly used object detection datasets. The Figure came from this survey paper.

The papers related to datasets used mainly in Object Detection are as follows.

  • [PASCAL VOC] The PASCAL Visual Object Classes (VOC) Challenge | Mark Everingham, et al. | [IJCV' 10] | [pdf]

  • [PASCAL VOC] The PASCAL Visual Object Classes Challenge: A Retrospective | Mark Everingham, et al. | [IJCV' 15] | [pdf] | [link]

  • [ImageNet] ImageNet: A Large-Scale Hierarchical Image Database | Jia Deng, et al. | [CVPR' 09] | [pdf]

  • [ImageNet] ImageNet Large Scale Visual Recognition Challenge | Olga Russakovsky, et al. | [IJCV' 15] | [pdf] | [link]

  • [COCO] Microsoft COCO: Common Objects in Context | Tsung-Yi Lin, et al. | [ECCV' 14] | [pdf] | [link]

  • [Open Images] The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale | A Kuznetsova, et al. | [arXiv' 18] | [pdf] | [link]

 

Contact & Feedback

If you have any suggestions about papers, feel free to mail me :)

 

posted @ 2019-03-30 15:11  Augustone  阅读(1088)  评论(0)  编辑  收藏  举报