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