Foundational Question Generation for Video Question Answering via an Embedding-Integrated Approach

• URL: http://arxiv.org/abs/2511.17618v1
• Date: Tue, 18 Nov 2025 13:45:50 GMT
• Title: Foundational Question Generation for Video Question Answering via an Embedding-Integrated Approach
• Authors: Ju-Young Oh,
• Abstract summary: We introduce Foundational Question Generation for Video Questioning via an Embedding-Integrated Approach (FIQ)<n>FIQ is a framework designed to enhance the reasoning capability of VQA models by improving their foundational comprehension of video content.<n> Experimental results on the SUTD-TrafficQA dataset demonstrate that FIQ achieves state-of-the-art performance, surpassing existing baseline approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional VQA approaches primarily rely on question-answer (Q&A) pairs to learn the spatio-temporal dynamics of video content. However, most existing annotations are event-centric, which restricts the model's ability to capture the comprehensive context of a scene. The lack of fundamental information such as object categories, spatial configurations, and descriptive visual attributes prevents the model from forming a complete understanding of the environment, ultimately limiting its generalization and reasoning capability. In this paper, we introduce Foundational Question Generation for Video Question Answering via an Embedding-Integrated Approach (FIQ), a framework designed to enhance the reasoning capability of VQA models by improving their foundational comprehension of video content. FIQ generates Q&A pairs from descriptive information extracted directly from videos, thereby enriching the dataset with core scene-level attributes. These generated pairs help the model develop a more holistic understanding of the video, leading to improved generalizability and reasoning performance. In addition, we propose a VQ-CAlign module that aligns task-specific question embeddings with corresponding visual features, preserving essential contextual cues and enhancing adaptability to downstream tasks. Experimental results on the SUTD-TrafficQA dataset demonstrate that FIQ achieves state-of-the-art performance, surpassing existing baseline approaches.

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