Text Modality
Text modality research explores how textual information can be effectively integrated with other data modalities (e.g., images, audio, video) to improve the performance and capabilities of AI models. Current research focuses on developing multimodal models using transformer architectures and diffusion models, often incorporating techniques like prompt tuning and meta-learning to enhance controllability and generalization. This work is significant because it enables more sophisticated AI systems capable of understanding and generating complex information across various data types, with applications ranging from improved medical diagnosis to more realistic virtual environments.
Papers
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs
Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction
Ying-Ting Yeh, Janghoon Ock, Amir Barati Farimani
Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text
Ayat Najjar, Huthaifa I. Ashqar, Omar Darwish, Eman Hammad
Visual Large Language Models for Generalized and Specialized Applications
Yifan Li, Zhixin Lai, Wentao Bao, Zhen Tan, Anh Dao, Kewei Sui, Jiayi Shen, Dong Liu, Huan Liu, Yu Kong
QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
Binita Saha, Utsha Saha, Muhammad Zubair Malik
Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues
X. Feng, D. Zhang, S. Hu, X. Li, M. Wu, J. Zhang, X. Chen, K. Huang
A Comparative Study of Machine Unlearning Techniques for Image and Text Classification Models
Omar M. Safa, Mahmoud M. Abdelaziz, Mustafa Eltawy, Mohamed Mamdouh, Moamen Gharib, Salaheldin Eltenihy, Nagia M. Ghanem, Mohamed M. Ismail