目标检测 Object Detection

本文收集目标检测相关论文，会在之后持续更新。

Leaderboard

Object Detector

Detector	Backbone	VOC	COCO	Paper

Pedestrain Detector

Detector	Backbone	Caltech(MR-2)	Caltech(MR-4)	Paper

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Paper

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Object Detection

RoI

RepPoints: Point Set Representation for Object Detection

• arXiv: http://arxiv.org/abs/1904.11490

CornerNet-Lite: Efficient Keypoint Based Object Detection

• arXiv: https://arxiv.org/abs/1904.08900
• github: https://github.com/princeton-vl/CornerNet-Lite

Objects as Points

• arXiv: https://arxiv.org/pdf/1904.07850.pdf
• github: https://github.com/xingyizhou/CenterNet

DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors

• arXiv: http://arxiv.org/abs/1904.06883
• note: Note-DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors

FoveaBox: Beyond Anchor-based Object Detector

• arXiv: https://arxiv.org/abs/1904.03797

High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection

• arXiv: https://arxiv.org/abs/1904.02948
• github: https://github.com/liuwei16/CSP
• pub: CVPR 2019

FCOS: Fully Convolutional One-Stage Object Detection

• arXiv: https://arxiv.org/abs/1904.01355
• github: https://github.com/tianzhi0549/FCOS

Feature Selective Anchor-Free Module for Single-Shot Object Detection

• arXiv: http://arxiv.org/abs/1903.00621

Bottom-up Object Detection by Grouping Extreme and Center Points

• arXiv: https://arxiv.org/abs/1901.08043
• github: https://github.com/xingyizhou/ExtremeNet

Region Proposal by Guided Anchoring

• arXiv: http://arxiv.org/abs/1901.03278
• pub: CVPR 2019

CornerNet: Detecting Objects as Paired Keypoints

• arXiv: https://arxiv.org/abs/1808.01244
• github: https://github.com/princeton-vl/CornerNet

DeNet Scalable Real-Time Object Detection With Directed Sparse Sampling

• arxiv: https://arxiv.org/abs/1703.10295
• pub: ICCV 2017
• [note](2017 - Tychsen-Smith, Petersson - DeNet Scalable Real-Time Object Detection With Directed Sparse Sampling.md)论文笔记《DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling》

Feature

Libra R-CNN: Towards Balanced Learning for Object Detection

• paper: https://arxiv.org/abs/1904.02701
• code: https://github.com/open-mmlab/mmdetection
• pub: CVPR 2018

$\star$ Multi-scale Location-aware Kernel Representation for Object Detection

• arxiv: https://arxiv.org/abs/1804.00428
• pub: CVPR 2018
• github: https://github.com/Hwang64/MLKP

Post process

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

• arXiv: https://arxiv.org/abs/1809.08545
• github: https://github.com/yihui-he/softer-NMS

Soft-NMS – Improving Object Detection With One Line of Code

• arxiv: https://arxiv.org/abs/1704.04503
• pub: ICCV 2017
• github: https://github.com/bharatsingh430/soft-nms
• [note](2017 - Bodla et al. - Soft-NMS – Improving Object Detection With One Line of Code.md) 论文笔记《Soft-NMS – Improving Object Detection With One Line of Code》

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Pedestrian Detection

PCN: Part and Context Information for Pedestrian Detection with CNNs

• arxiv: https://arxiv.org/abs/1804.04483
• pub: BMVC 2017

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

• arxiv: https://arxiv.org/abs/1804.02047
• github: https://github.com/yueruchen/Pedestrian-Synthesis-GAN

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

• arxiv: https://arxiv.org/abs/1803.05347

Repulsion Loss: Detecting Pedestrians in a Crowd

• arxiv: https://arxiv.org/abs/1711.07752
• pub: CVPR 2018

Illuminating Pedestrians via Simultaneous Detection & Segmentation

• arxiv: https://arxiv.org/abs/1706.08564
• pub: ICCV 2017
• paper

What Can Help Pedestrian Detection?

• arxiv: https://arxiv.org/abs/1705.02757
• pub: CVPR 2017

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

• arxiv: https://arxiv.org/abs/1703.06283
• pub: CVPR 2017

CityPersons: A Diverse Dataset for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1702.05693
• [note](2017 - Zhang, Benenson, Schiele - CityPersons A Diverse Dataset for Pedestrian Detection.md)论文笔记《CityPersons: A Diverse Dataset for Pedestrian Detection》

Deep Multi-Camera People Detection

• arxiv: https://arxiv.org/abs/1702.04593
• pub: International Conference on Machine Learning and Applications

Multispectral Deep Neural Networks for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1611.02644
• pub: BMVC 2016 oral

Fused DNN A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection

• arxiv: https://arxiv.org/abs/1610.03466
• pub: WACV 2017
• [note](2017 - Du et al. - Fused DNN A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection.md)

Is Faster R-CNN Doing Well for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1607.07032
• pub: ECCV 2016
• [note](2016 - Zhang et al. - Is Faster R-CNN Doing Well for Pedestrian Detection.md)论文笔记《Is Faster R-CNN Doing Well for Pedestrian Detection?》

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Video Object Detection

Learning Correspondence from the Cycle-Consistency of Time

• pub: CVPR 2019 (oral)
• github: https://github.com/xiaolonw/TimeCycle

Detect-and-Track: Efficient Pose Estimation in Videos

• arxiv: https://arxiv.org/abs/1712.09184
• pub: CVPR 2018
• github: https://github.com/facebookresearch/DetectAndTrack
• intro: 1st ICCV 2017 PoseTrack

Flow-Guided Feature Aggregation for Video Object Detection

• arxiv: https://arxiv.org/abs/1703.10025
• pub: ICCV 2017
• github: https://github.com/msracver/Flow-Guided-Feature-Aggregation
• [note](2017 - Zhu et al. - Flow-Guided Feature Aggregation for Video Object Detection.md)论文笔记《Deep Feature Flow for Video Recognition》

Deep Feature Flow for Video Recognition

• arxiv: https://arxiv.org/abs/1611.07715
• pub: CVPR 2017
• [note](2017 - Zhu et al. - Deep Feature Flow for Video Recognition.md)论文笔记《Deep Feature Flow for Video Recognition》

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Segmentation

YOLACT: Real-time Instance Segmentation

• arxiv: https://arxiv.org/abs/1904.02689
• github: https://github.com/dbolya/yolact

Rethinking Atrous Convolution for Semantic Image Segmentation

• arxiv: https://arxiv.org/abs/1706.05587
• [note](2017 - Chen et al. - Rethinking Atrous Convolution for Semantic Image Segmentation.md)论文笔记《Rethinking Atrous Convolution for Semantic Image Segmentation》