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

Ju-Young Oh

TL;DR

The paper tackles the limitation of event-centric annotations in video question answering by introducing FIQ, a framework that generates foundational Q&A pairs describing core visual attributes from videos to enrich scene-level understanding. It introduces VQ-CAlign to fuse task-specific question embeddings with visual features, preserving contextual cues and improving down-stream adaptability. Evaluated on the SUTD-TrafficQA dataset, FIQ achieves state-of-the-art performance and shows notable gains across multiple reasoning tasks, with LM-based Q&A generation (especially GPT-based) providing the strongest improvements. The approach advances multimodal reasoning by integrating foundational visual knowledge with targeted linguistic guidance, enabling better generalization to complex, real-world video QA scenarios.

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.

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

TL;DR

The paper tackles the limitation of event-centric annotations in video question answering by introducing FIQ, a framework that generates foundational Q&A pairs describing core visual attributes from videos to enrich scene-level understanding. It introduces VQ-CAlign to fuse task-specific question embeddings with visual features, preserving contextual cues and improving down-stream adaptability. Evaluated on the SUTD-TrafficQA dataset, FIQ achieves state-of-the-art performance and shows notable gains across multiple reasoning tasks, with LM-based Q&A generation (especially GPT-based) providing the strongest improvements. The approach advances multimodal reasoning by integrating foundational visual knowledge with targeted linguistic guidance, enabling better generalization to complex, real-world video QA scenarios.

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.

Paper Structure

This paper contains 19 sections, 4 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Video Question Answering
  4. General Question Generation
  5. Methods
  6. Preliminaries
  7. Fundamental Question Generation
  8. Candidate Answer Extraction
  9. Question Generation
  10. Answer Validation
  11. Textual Representation Refinement
  12. Integration of Question Embeddings
  13. Experiments
  14. Setup
  15. Hyperparameters
  16. ...and 4 more sections

Figures (3)

  • Figure 1: The existing dataset only focuses on event-centric information of video, but not on fundamental information of video such as shape, color, and direction of objects.
  • Figure 2: Overall architecture of FIQ. It consists of four pivotal sub-processes. Q&A pair which contains the general information of video first generated using language model such as T5 t5, and GPT gpt4. The frozen text encoder takes these generate Q&A pairs with the original dataset as an input, and each question embeddings and answer candidate embeddings are passed to the Trans-Decoder and VQ-CAlign. The frozen image encoder takes video data as input, and extracted visual features are passed to VQ-CAlign with question embeddings. Both modalities are merged, and passed to the Ans-Decoder, which fuses visual and textual information to align the temporal information.
  • Figure 3: Comparison between different LM-based Q&A generation (T5, GPT) methods on SUTD-TrafficQA.