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Updated: Jan 14, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Causality guided co-attention network for visual question answering.

Jiali Miao1, Kui Yu1, Baofu Fang2

  • 1Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, 230601, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 17, 2025
PubMed
Summary
This summary is machine-generated.

The Causality Guided Co-attention Network (CGCN) improves Visual Question Answering (VQA) by distinguishing influential and supportive object features. This approach enhances multimodal learning for better image-based question answering.

Keywords:
CausalityCo-attention networkMarkov blanketMulti-modal representationVisual question answering

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Last Updated: Jan 14, 2026

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Visual Question Answering (VQA) is crucial for multimodal AI, leveraging visual and language understanding.
  • Current VQA models struggle with cluttered images, as self-attention mechanisms fail to focus on relevant objects.
  • Exclusive focus on relevant objects can be detrimental, while background features can sometimes aid performance.

Purpose of the Study:

  • To address the limitations of self-attention in VQA models with complex visual scenes.
  • To develop a novel network that effectively utilizes both relevant and supportive visual features.
  • To enhance the accuracy of VQA systems by improving multimodal representation learning.

Main Methods:

  • Proposed the Causality Guided Co-attention Network (CGCN), a hierarchical granular visual feature attention network.
  • Introduced a causal graph to model object feature relationships, categorizing them into Influential Object Features (IOFs) and Supportive Object Features (SOFs).
  • Developed a causality-inspired co-attention mechanism guided by causal graphs for cross-modal learning and a dual-output fusion method.

Main Results:

  • The CGCN significantly improved performance in Visual Question Answering tasks.
  • The proposed method demonstrated enhanced ability to focus on relevant visual information while incorporating supportive features.
  • Experimental results validated the effectiveness of the causality-guided approach in VQA.

Conclusions:

  • The CGCN offers a promising approach to overcome challenges in VQA, particularly in scenes with numerous overlapping objects.
  • Distinguishing and utilizing IOFs and SOFs effectively enhances multimodal feature representation and fusion.
  • The causality-guided co-attention mechanism represents a significant advancement in VQA model development.