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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Multi-modal adaptive gated mechanism for visual question answering.

Yangshuyi Xu1, Lin Zhang1, Xiang Shen1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai, China.

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|June 28, 2023
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Summary

This study introduces the multimodal adaptive gated mechanism (MAGM) model to improve visual question answering (VQA) by filtering noise and enhancing feature fusion. MAGM achieves superior performance on VQA 2.0 and GQA datasets.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Visual Question Answering (VQA) is a multimodal task requiring accurate feature extraction from images and text.
  • Existing VQA models often overlook modal interaction learning and noise introduction during fusion, impacting performance.
  • Attention mechanisms and multimodal fusion are common approaches, but can be suboptimal.

Purpose of the Study:

  • To propose a novel multimodal adaptive gated mechanism (MAGM) model for VQA.
  • To enhance the filtering of irrelevant noise information and obtain fine-grained modal features.
  • To improve the model's adaptive control over the contribution of image and text features for accurate answers.

Main Methods:

  • Incorporating an adaptive gate mechanism into intra- and inter-modality learning and modal fusion.
  • Designing self-attention gated and self-guided-attention gated units to filter noise in text and image features.
  • Developing an adaptive gated modal feature fusion structure for improved feature representation.

Main Results:

  • The MAGM model demonstrated superior performance compared to existing methods on benchmark datasets.
  • Achieved an overall accuracy of 71.30% on the VQA 2.0 dataset.
  • Achieved an overall accuracy of 57.57% on the GQA dataset.

Conclusions:

  • The proposed MAGM model effectively filters noise and enhances modal interaction in VQA.
  • The adaptive gating mechanism improves the model's ability to integrate multimodal features for better VQA accuracy.
  • MAGM represents a significant advancement in VQA research, offering improved performance and robustness.