近期学长在研究方向上给了我一些指导，在此总结整理一下。

• 学长整理的视频目标检测综述：https://blog.csdn.net/breeze_blows/article/details/105323491
• 学姐整理的视频目标检测综述：https://blog.csdn.net/sinat_31184961/article/details/103128931
• 学长的github：https://github.com/breezelj
• 学长整理的视频目标检测的文章：https://github.com/breezelj/video_object_detection_paper

视频目标检测论文

• RDN: Jiajun Deng, Yingwei Pan, Ting Yao, Wengang Zhou, Houqiang Li, and Tao Mei. “Relation Distillation Networks for Video Object Detection”. ICCV(2019).
• SELSA: Haiping Wu, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang. “Sequence Level Semantics Aggregation for Video Object Detection”. ICCV(2019).
• LLTR: Mykhailo Shvets, Wei Liu, Alexander C. Berg. “Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection”. ICCV(2019).
• MEGA: Yihong Chen, Yue Cao, Han Hu, Liwei Wang. “Memory Enhanced Global-Local Aggregation for Video Object Detection”. CVPR(2020).
• HVRNet: Mingfei Han, Yali Wang, Xiaojun Chang, and Yu Qiao Mining. “Mining Inter-Video Proposal Relations for Video Object Detection”. ECCV(2020).
• FGFA: Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei. “Flow-Guided Feature Aggregation for Video Object Detection”. ICCV(2017).

学长总结的目标检测论文(转)

基于anchor的目标检测主要可以分为two-stage detector；one-stage detector

1.two-stages：

• RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR014
• Fast RCNN. ICCV2015
• Faster RCNN: Towards real-time object detection with region proposal networks. NIPS2015
• Mask RCNN. ICCV2017
• Cascade RCNN: Delving into High Quality Object Detection. CVPR2018

2.one-stage:

• YOLOv1: You only look once: Unified, real-time object detection. CVPR2016
• Yolo9000: better, faster, stronger. CVPR2017
• YOLOv3: An Incremental Improvement
• SSD: Single shot multibox detector. ECCV2016
• Retinanet: Focal loss for dense object detection. ICCV2017

近年来兴起的anchor-free的detector

3.anchor-free：

• CornerNet: Detecting Objects as Paired Keypoints. ECCV2018.
• FCOS: Fully Convolutional One-Stage Object Detection. ICCV2019
• Centernet:Objects as Points/ Keypoint triplets for object detection ICCV2019

4.一些通用的目标检测结构

• OHEM: Training Region-based Object Detectors with Online Hard Example Mining. CVPR2016
• FPN: Feature Pyramid Networks for Object Detection. CVPR2017

5.目标检测平台（集成了众多目标检测算法的代码实现）

• mmdetection：https://github.com/open-mmlab/mmdetection
• detectron2：https://github.com/facebookresearch/detectron2

6.目标检测综述文章

• Object Detection in 20 Years: A Survey

• A Survey of Deep Learning-based Object Detection

• Recent Advances in Deep Learning for Object Detection

• https://github.com/hoya012/deep_learning_object_detection#2014