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]
转载本文请联系原作者获取授权，同时请注明本文来自周池春科学网博客。
链接地址：https://wap.sciencenet.cn/blog-3453120-1286075.html?mobile=1
收藏
