Thorray

ThorrayApollo

上海，中国 上海交通大学 @Thorray

  1. 论文阅读-10：通过ImageNet训练的CNN是纹理偏置；增加形状偏置增加准确率和鲁棒性

    2020/02/25

  2. 论文阅读-9：BASNet:关注边缘的显著性物体检测

    2020/02/19

  3. 论文阅读-8：用于实时实例分割的Deep Snake

    2020/02/10

  4. 论文阅读-7：DeepCO3：基于共峰搜索和共显著性检测的深度实例共分割

    2020/02/09

  5. 论文阅读-6：DHSNet：深度层次显著性目标检测网络

    2020/02/08

  6. 论文阅读-5：基于边界细化和全局上下文的深度网络显著性检测

    2020/02/05

  7. 论文阅读-4：基于最小显著区域回归的螺旋共享网络显著性检测

    2020/02/04

  8. TA-4：C++中malloc/free与new/delete浅析

    2020/02/01

  9. 论文阅读-3：用于显著性目标检测的金字塔特征注意网络

    2020/01/31

  10. 论文阅读-2：从单一场景的深度光场驱动显著性目标检测

    2020/01/30

My Popular Repositories

Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection

There is a paper in CVPR 2019 about the saliency detection. The source code of this paper is using the keras framework. We transform them into pytorch framework.

9 2

CS137-TA

4 0

2019-Postgraduate-Mathematical-Modeling-Competition-A

无线智能传播模型 ：利用模型准确预测在新环境下无线信号覆盖强度，减少网络 建设成本，提高网络建设效率

3 2

Big-Data-Processing

This course mainly introduces big data storage systems. For both structured and unstructured data, this course illustrates them in storage and database tracks, respectively. Particularly, tranditional big data storage systems are introduced in this course, such as hadoop (HDFS,HBase), Google GFS/Chubby/BigTable, Amazon S3, Microsoft Azure Storage, Ali Cloud, etc. Critical issues like data reliability, data consistency, metadata management are well illustrated in this course. The course aims to help students to understand how file systems/databases really work for big data storage.

2 0

thorraysjtu.github.io

0 0