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deep learning object detection

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

Last updated: 2019/05/23

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.
2019/april - remove author's names and update ICLR 2019 & CVPR 2019 papers.
2019/may - update CVPR 2019 papers.

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.

Detector VOC07 (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

2017

2018

2019

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

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

  • [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR' 19] |[pdf]

  • Feature Intertwiner for Object Detection | [ICLR' 19] |[pdf]

  • [GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR' 19] |[pdf]

  • Automatic adaptation of object detectors to new domains using self-training | [CVPR' 19] |[pdf]

  • [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR' 19] |[pdf]

  • Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR' 19] |[pdf]

  • [ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR' 19] |[pdf] | [official code - pytorch]

  • [C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection | [CVPR' 19] |[pdf] | [official code - torch]

  • [ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR' 19] |[pdf]

  • Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR' 19] |[pdf] | [official code - caffe2]

  • Activity Driven Weakly Supervised Object Detection | [CVPR' 19] |[pdf] | [official code - pytorch]

  • Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR' 19] |[pdf]

  • Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR' 19] |[pdf] | [official code - pytorch]

  • [NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR' 19] |[pdf]

  • [Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR' 19] |[pdf]

  • Point in, Box out: Beyond Counting Persons in Crowds | [CVPR' 19] |[pdf]

  • Locating Objects Without Bounding Boxes | [CVPR' 19] |[pdf]

  • Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR' 19] |[pdf]

  • Towards Universal Object Detection by Domain Attention | [CVPR' 19] |[pdf]

  • Exploring the Bounds of the Utility of Context for Object Detection | [CVPR' 19] |[pdf]

  • What Object Should I Use? - Task Driven Object Detection | [CVPR' 19] |[pdf]

  • Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR' 19] |[pdf]

  • Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR' 19]

  • Fully Quantized Network for Object Detection | [CVPR' 19] |[pdf]

  • Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR' 19]

  • Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR' 19]

  • [Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR' 19]

  • Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR' 19] |[pdf]

  • Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR' 19] |[pdf]

  • Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR' 19]

  • [MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR' 19]

  • You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR' 19] |[pdf]

  • Object detection with location-aware deformable convolution and backward attention filtering | [CVPR' 19]

  • Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR' 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 | [IJCV' 10] | [pdf]

  • [PASCAL VOC] The PASCAL Visual Object Classes Challenge: A Retrospective | [IJCV' 15] | [pdf] | [link]

  • [ImageNet] ImageNet: A Large-Scale Hierarchical Image Database| [CVPR' 09] | [pdf]

  • [ImageNet] ImageNet Large Scale Visual Recognition Challenge | [IJCV' 15] | [pdf] | [link]

  • [COCO] Microsoft COCO: Common Objects in Context | [ECCV' 14] | [pdf] | [link]

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

Contact & Feedback

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

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A paper list of object detection using deep learning.

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