New for: D1, D2, D3, INET, D4, D5, D6
At the core of my research in Multimodal Information Extraction (IE) is to bring such deep semantic understanding ability to the multimodal world. My work opens up a new research direction Event-Centric Multimodal Knowledge Acquisition to transform traditional entity-centric single-modal knowledge into event-centric multi-modal knowledge. Such a transformation poses two significant challenges: (1) understanding multimodal semantic structures that are abstract (such as events and semantic roles of objects): I will present my solution of zero-shot cross-modal transfer (CLIP-Event), which is the first to model event semantic structures for vision-language pretraining, and supports zero-shot multimodal event extraction for the first time; (2) understanding long-horizon temporal dynamics: I will introduce Event Graph Model, which empowers machines to capture complex timelines, intertwined relations and multiple alternative outcomes. I will also show its positive results on long-standing open problems, such as timeline generation, meeting summarization, and question answering. Such Event-Centric Multimodal Knowledge starts the next generation of information access, which allows us to effectively access historical scenarios and reason about the future. I will lay out how I plan to grow a deep semantic understanding of language world and vision world, moving from concrete to abstract, from static to dynamic, and ultimately from perception to cognition.
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