Researcher Portfolio
Hong, Xudong
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Researcher Profile
Position: Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons228992
Publications
: Shvetsova, N., Kukleva, A., Hong, X., Rupprecht, C., Schiele, B., & Kuehne, H. (2024). HowToCaption: Prompting LLMs to Transform Video Annotations at Scale. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds. ), Computer Vision -- ECCV 2024 (pp. 1-18). Berlin: Springer. doi:10.1007/978-3-031-72992-8_1. [PubMan] : Hong, X., Sayeed, A., Mehra, K., Demberg, V., & Schiele, B. (2023). Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences. Transactions of the Association for Computational Linguistics, 11, 565-581. doi:10.1162/tacl_a_00553. [PubMan] : Hong, X., Demberg, V., Sayeed, A., Zheng, Q., & Schiele, B. (2023). Visual Coherence Loss for Coherent and Visually Grounded Story Generation. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds. ), Findings of the Association for Computational Linguistics (pp. 9456-9470). Stroudsburg, PA: ACL. doi:10.18653/v1/2023.findings-acl.603. [PubMan] : Cao, H., Hong, X., Tost, H., Meyer-Lindenberg, A., & Schwarz, E. (2022). Advancing Translational Research in Neuroscience through Multi-task Learning. Frontiers in Psychiatry, 13: 993289. doi:10.3389/fpsyt.2022.993289. [PubMan] : Pu, D., Hong, X., Lin, P.-J., Chang, E., & Demberg, V. (2022). Two-Stage Movie Script Summarization: An Efficient Method For Low-Resource Long Document Summarization. In K. Mckeown (Ed. ), Proceedings of The Workshop on Automatic Summarization for Creative Writing (pp. 57-66). Stroudsburg PA: ACL. Retrieved from https://aclanthology.org/2022.creativesumm-1.9. [PubMan] : Hong, X., Shetty, R., Sayeed, A., Mehra, K., Demberg, V., & Schiele, B. (2020). Diverse and Relevant Visual Storytelling with Scene Graph Embeddings. In Proceedings of the 24th Conference on Computational Natural Language Learning (pp. 420-430). Stroudsburg, PA: ACL. doi:10.18653/v1/2020.conll-1.34. [PubMan] : Hong, X., Chang, E., & Demberg, V. (2019). Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders. In S. Mille, A. Belz, B. Bohnet, Y. Graham, & L. Wanner (Eds. ), Multilingual Surface Realisation (pp. 75-80). Stroudsburg, PA: ACL. doi:10.18653/v1/D19-6310. [PubMan]