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deep learning object detection

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

Last updated: 2018/10/25

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)
2018/10/05 - update 3 papers.
2018/10/12 - update 1 paper.
2018/10/25 - update performance table and add 1 paper.

paper list from 2014 to now(2018)

performance table

Detector VOC07 (mAP@IoU=0.5) VOC12 (mAP@IoU=0.5) COCO (mAP) Published In
R-CNN 58.5 - - CVPR'14
OverFeat - - - ICLR'14
MultiBox 29.0 - - CVPR'14
SPP-Net 59.2 - - ECCV'14
MR-CNN 78.2 (07+12) 73.9 (07+12) - ICCV'15
AttentionNet - - - ICCV'15
Fast R-CNN 70.0 (07+12) 68.4 (07++12) - ICCV'15
Faster R-CNN 73.2 (07+12) 70.4 (07++12) - 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) - ECCV'16
GBDNet 77.2 (07+12) - 27.0 ECCV'16
CPF 76.4 (07+12) 72.6 (07++12) - ECCV'16
MS-CNN - - - ECCV'16
R-FCN 79.5 (07+12) 77.6 (07++12) 29.9 NIPS'16
PVANET - - - NIPSW'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) - 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) - CVPR'17
DCN - - - ICCV'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
Mask R-CNN - - - ICCV'17
DSOD 77.7 (07+12) 76.3 (07++12) - ICCV'17
SMN 70.0 - - ICCV'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
MegDet - - - CVPR'18
RFBNet 82.2 (07+12) - - ECCV'18

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

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A paper list of object detection using deep learning.

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