Our work in multimodal learning includes stepwise story illustration using images, news image caption generation, multimodal fake news detection, and multimodal event representation learning.
Vishwash Batra, Lin Gui, Wenjia Zhang
- W. Zhang, L. Gui and Y. He. Supervised Contrastive Learning for Multi-modal Unreliable News Detection in COVID-19 Pandemic, The 30th ACM International Conference on Information and Knowledge Management (CIKM), Nov. 2021.
- D. Zhou, K. Sun, M. Hu and Y. He. Image Generation from Text with Entity Information Fusion, Knowledge-Based Systems, Vol. 227, 107200, 2021.
- L. Zhang, D. Zhou, Y. He and Z. Yang. MERL: Multimodal Event Representation Learning in Heterogeneous Embedding Spaces, The 35th AAAI Conference on Artificial Intelligence (AAAI), Feb. 2021.
- V. Batra, A. Haldar, Y. He, H. Ferhatosmanoglu, G. Vogiatzis and T. Guha. Variational Recurrent Sequence-to-Sequence Retrieval for Stepwise Illustration. The 42nd European Conference on Information Retrieval (ECIR), Lisbon, Portugal, Apr. 2020.
- M. Hu, D. Zhou and Y. He. Variational Conditional GAN for Fine-grained Controllable Image Generation. The 11th Asian Conference on Machine Learning (ACML), Nagoya, Japan, Nov. 2019.
- V. Batra, Y. He and G. Vogiatzis. A Deep Learning Approach to Automatic Caption Generation for News Images, The 11th International Conference on Language Resources and Evaluation (LREC), Miyazaki, Japan, May 2018.