Knowledge-based video question answering with unsupervised scene descriptions

Abstract

To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen. Inspired by this behaviour, we design ROLL, a model for knowledge-based video story question answering that leverages three crucial aspects of movie understanding: dialog comprehension, scene reasoning, and storyline recalling. In ROLL, each of these tasks is in charge of extracting rich and diverse information by 1) processing scene dialogues, 2) generating unsupervised video scene descriptions, and 3) obtaining external knowledge in a weakly supervised fashion. To answer a given question correctly, the information generated by each inspired-cognitive task is encoded via Transformers and fused through a modality weighting mechanism, which balances the information from the different sources. Exhaustive evaluation demonstrates the effectiveness of our approach, which yields a new state-of-the-art on two challenging video question answering datasets: KnowIT VQA and TVQA+.

Publication
Proceedings - European Conference on Computer Vision
Noa Garcia
Noa Garcia
Specially-Appointed Assistant Professor

Her research interests lie in computer vision and machine learning applied to visual retrieval and joint models of vision and language for high-level understanding tasks.

Yuta Nakashima
Yuta Nakashima
Associate Professor

Yuta Nakashima is an associate professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.

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