pliang279/awesome-multimodal-ml
Reading list for research topics in multimodal machine learning
This reading list helps AI researchers and students navigate the rapidly evolving field of multimodal machine learning. It curates academic papers, course materials, and workshops, covering core areas like multimodal representations and fusion, along with applications across various domains. Researchers and graduate students interested in developing AI systems that process and understand multiple data types (like text, images, and audio) would find this resource invaluable.
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Use this if you are an AI researcher or student seeking a comprehensive, organized collection of academic resources to deepen your understanding or identify research gaps in multimodal machine learning.
Not ideal if you are looking for ready-to-use open-source code libraries or practical tutorials for implementing multimodal models without a strong theoretical background.
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