
Academic Area:
Computer Vision and Multimedia retrieval
Research Interests:
- Computer Vision and Pattern Recognition
- Image Retrieval and Multi-modal Retrieval
- Deep Hashing
- Lifelong Learning
Bio:
Educational Background:
- Visiting Scholar, School of Computer Science and Engineering, Nanyang Technological University (09/2019– 10/2020)
- Ph.D., Computer Science and Technology, Nanjing University of Aeronautics and astronautics (09/2014 – 12/2020)
- BE, Computer Science and Technology (Information Engineering), Zhengzhou University (09/2010 – 06/2014)
Journal Articles:
- Ge Song, Xiaoyang Tan, Jun Zhao. Deep Robust Multilevel Semantic Hashing for Multi-Label Cross-Modal Retrieval. Accepted by Pattern Recognition (PR), 2021.
- Ge Song, Xiaoyang Tan. Real-world Cross-modal Retrieval via Sequential Learning. IEEE Transactions on Multimedia (TMM), 2020, early access, doi: 10.1109/TMM.2020.3002177
- Ge Song, Xiaoyang Tan. Deep code operation network for multi-label image retrieval. Computer Vision and Image Understanding (CVIU), 193 (2020): 102916.
- Ge Song, Dong Wang, Xiaoyang Tan. Deep Memory Network for Cross-modal Retrieval. IEEE Transactions on Multimedia (TMM), 21(5):1261-1275, 2019.
- Dong Wang, Ge Song, Xiaoyang Tan. Bayesian denoising hashing for robust image retrieval. Pattern Recognition (PR), 86 (2019): 134-142.
- Ge Song, Xiaoyang Tan. Hierarchical deep hashing for image retrieval. Frontiers of Computer Science (FCS), 11.2 (2017): 253-265.
Conference Papers:
- Ge Song, Xiaoyang Tan. Cross-modal Retrieval via Memory Network. The 28th British Machine Vision Conference (BMVC), 2017, London, UK, September 4-7.
- Ge Song, Xiaoyang Tan. Learning Multilevel Semantic Similarity for Large-Scale Multi-Label Image Retrieval. The 8th ACM International Conference on Multimedia Retrieval (ICMR), 2018, 64-72, Yokohama, Japan, June 11–14.
Ge Song, Xiaoyang Tan. Sequential Learning for Cross-Modal Retrieval. The IEEE International Conference on Computer Vision Workshop (ICCVW), 2019, Seoul, Korea, October 27-November 2.