厦门大学人工智能研究院
曹刘娟

厦门大学    副教授,博士生导师

媒体分析与计算实验室

地址:厦门大学海韵校区 行政C楼#705 邮编:361005

电子邮件:caoliujuan@xmu.edu.cn


 

  ■

名称:WS-JDS

链接https://github.com/shenyunhang/WS-JDS

引文:Yunhang Shen, Rongrong Ji*, Yan Wang, Yongjian Wu, Liujuan Cao. Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).

简介:This package is the implementation of our efficient and effective framework termed Weakly Supervised Joint Detection and Segmentation (WS-JDS). It includes the training and testing codes on various datasets. It also includes serveral the state-of-the-art WSOD methods.


  ■

名称:Bi-MHGL

链接https://github.com/cfh3c/BiMHGL

引文:Rongrong Ji, Fuhai Chen, Liujuan Cao*, Yue Gao*. Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph Learning. IEEE Transactions on Multimedia (TMM).

简介:This is the implementation of bi-Layer multimodal hypergraph learning for microblog sentiment prediction. It contains transductive learning, evaluation and inference. The entrances are in run_CV_gridsearch.m. BiHG_learning2.m presents the core part of BiMHGL.


  ■

名称:GAL

链接https://github.com/ShaohuiLin/GAL

引文:Shaohui Lin, Rongrong Ji*, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, David Doermann. Towards Optimal Structured CNN Pruning via Generative Adversarial Learning. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).

简介:PyTorch implementation for GAL, which solves the compression problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end-to-end manner.