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Visual dialog with semantic consistency: An external knowledge-driven approach.

Shanshan Du1, Hanli Wang1

  • 1The College of Electronic and Information Engineering, Tongji University, Shanghai, China; The School of Computer Science and Technology, Tongji University, Shanghai, China; The Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, Shanghai, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new visual dialog model (SCVD+) that uses structured scene graphs and external knowledge to improve accuracy. It addresses bias and knowledge issues in multimodal AI for better human-machine interaction.

Keywords:
Cross-modal knowledge reasoningMultimodal knowledge graphSemantic consistencyVisual dialog

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Vision

Background:

  • Visual dialog, a key part of intelligent human-machine interaction, faces challenges in multi-turn question answering based on visual context and dialog history.
  • Existing models suffer from bias in multimodal modeling, including information asymmetry and representation inconsistency, leading to incomplete understanding and biased decisions.
  • Reliance on external knowledge introduces noise and reduces accuracy due to poor quality and limited diversity.

Purpose of the Study:

  • To propose a novel semantic consistency visual dialog model enhanced by external knowledge (SCVD+) to address existing challenges.
  • To mitigate information asymmetry and representation inconsistency in multimodal modeling for visual dialog.
  • To improve the accuracy, coherence, and reasoning capabilities of visual dialog systems.

Main Methods:

  • Constructing fine-grained structured visual and textual scene graphs to capture object relationships and word associations.
  • Integrating external commonsense knowledge to reduce representation inconsistency and enhance model interpretability.
  • Employing a dual-level knowledge fusion and reasoning strategy to integrate implicit clues from large pre-trained models with explicit scene graph information.

Main Results:

  • The proposed SCVD+ model effectively addresses information asymmetry and representation inconsistency.
  • Integration of external knowledge and a novel fusion strategy enhances the diversity of knowledge and reasoning capabilities.
  • Experimental results on VisDial v0.9, VisDial v1.0, and OpenVisDial 2.0 datasets demonstrate the method's effectiveness.

Conclusions:

  • The SCVD+ model offers a significant advancement in visual dialog systems by improving semantic consistency and knowledge integration.
  • The approach enhances multimodal understanding and decision-making, paving the way for more robust intelligent human-machine interaction.
  • The study highlights the importance of structured scene graphs and diverse external knowledge for accurate and coherent visual dialog responses.