Multimodal Sequence

Multimodal sequence analysis focuses on understanding and generating sequences of data encompassing diverse modalities like text, images, audio, and video. Current research emphasizes developing unified model architectures, often based on transformers, that can effectively process and integrate information from these disparate sources, addressing challenges like unaligned data and information redundancy through techniques such as mutual information maximization and disentanglement. This field is crucial for advancing artificial intelligence capabilities in areas like video understanding, sentiment analysis, and multimodal generation, leading to more robust and contextually aware AI systems.