Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond

• URL: http://arxiv.org/abs/2410.05608v1
• Date: Tue, 8 Oct 2024 01:41:56 GMT
• Title: Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond
• Authors: Soyeon Caren Han, Feiqi Cao, Josiah Poon, Roberto Navigli,
• Abstract summary: This tutorial aims to equip researchers, practitioners, and newcomers with the knowledge and skills to leverage multimodal AI. We will cover the latest multimodal datasets and pretrained models, including those beyond vision and language. Hands-on laboratories will offer practical experience with state-of-the-art multimodal models.
• Score: 51.141270065306514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This tutorial explores recent advancements in multimodal pretrained and large models, capable of integrating and processing diverse data forms such as text, images, audio, and video. Participants will gain an understanding of the foundational concepts of multimodality, the evolution of multimodal research, and the key technical challenges addressed by these models. We will cover the latest multimodal datasets and pretrained models, including those beyond vision and language. Additionally, the tutorial will delve into the intricacies of multimodal large models and instruction tuning strategies to optimise performance for specific tasks. Hands-on laboratories will offer practical experience with state-of-the-art multimodal models, demonstrating real-world applications like visual storytelling and visual question answering. This tutorial aims to equip researchers, practitioners, and newcomers with the knowledge and skills to leverage multimodal AI. ACM Multimedia 2024 is the ideal venue for this tutorial, aligning perfectly with our goal of understanding multimodal pretrained and large language models, and their tuning mechanisms.

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