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Related Experiment Video

Updated: Oct 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Learning to Reason on Tree Structures for Knowledge-Based Visual Question Answering.

Qifeng Li1,2,3, Xinyi Tang1,3, Yi Jian1,3

  • 1Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai 200083, China.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a question-guided tree structure with a knowledge base (QGTSKB) for improved visual question answering. The novel model enhances reasoning accuracy and interpretability in image-based queries.

Keywords:
attention mechanismcompositional reasoningknowledge baseneural module networktree structure

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Knowledge-based visual question answering (KB-VQA) is crucial for image comprehension.
  • Existing methods struggle with effective feature fusion and lack explicit visual reasoning processes.

Purpose of the Study:

  • To propose a novel reasoning model, the question-guided tree structure with a knowledge base (QGTSKB), for KB-VQA.
  • To address limitations in visual reasoning and parameter efficiency in current approaches.

Main Methods:

  • Developed a QGTSKB model comprising four neural module networks: attention, gated reasoning, fusion reasoning, and knowledge-based fact models.
  • Employed a tree structure for visual analysis and reasoning, integrating image features, question embeddings, and an external knowledge base.

Main Results:

  • The QGTSKB model demonstrated superior performance on the VQA v2.0 and CLVER datasets compared to existing methods.
  • Visual reasoning experiments confirmed the model's interpretability.

Conclusions:

  • The proposed QGTSKB model effectively performs visual analysis and reasoning for KB-VQA.
  • The model offers enhanced accuracy and interpretability, advancing the field of visual question answering.