题目：深度学习目标检测汇总----yolov3

主讲人：朱浩

时间：2021年5月12日晚上9:00～10:00

地点：工程学院409

简介：1）现有目标检测模型梳理

          2）yolo系列模型的原理及实现

参考文献:

1、YOLOv3: An Incremental Improvement

2、Rich feature hierarchies for accurate object detection and semantic segmentation | Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik | [CVPR’ 14]

3、OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | Pierre Sermanet, et al. | [ICLR’ 14]

4、Fast R-CNN | Ross Girshick | [ICCV’ 15]

5、Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Shaoqing Ren, et al. | [NIPS’ 15]

6、Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR’ 16]

7、You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR’ 16]

8、Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV’ 16]

9、Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS’ 16]

10、Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR’ 17]

11、Feature Pyramid Networks for Object Detection | Tsung-Yi Lin, et al. | [CVPR’ 17]

12、Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV’ 17]

13、Kaiming He, et al. | [ICCV’ 17]

14、An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv’ 18]

15、Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR’ 18]

16、A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Qijie Zhao, et al. | [AAAI’ 19]