Work

Optimising Multimodal QA with Streamlined Video Processing

Year: 2026

Type: preprint

Abstract: Multimodal Question Answering over video content — answering natural-language questions about events, objects, or dialogue occurring in a video — typically requires processing dense, high-frame-rate video alongside audio and text, which is computationally expensive and often redundant, since most frames contain little new information relevant to any given question. This project proposes an optimized multimodal video QA pipeline that streamlines video processing through intelligent frame selection and efficient multimodal fusion, aiming to substantially reduce computational cost while maintaining or improving question-answering accuracy compared to dense, unoptimized processing baselines.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22022000

Author: GUPTA, ADISHREE

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7203920583

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