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Related Experiment Video

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Adversarial Learning with Bidirectional Attention for 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)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Visual Question Answering (VQA) model using adversarial learning and bidirectional attention to improve accuracy by effectively filtering irrelevant image features. The enhanced model outperforms existing attention-based methods on benchmark datasets.

Keywords:
adversarial learningattention mechanismattention visualizationbidirectional attentionfeature fusionfeature selectionvisual question answering

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional Visual Question Answering (VQA) models often struggle with irrelevant image features due to limitations in attention mechanisms.
  • Existing VQA approaches using question-oriented attention can lead to redundant image feature extraction, hindering performance.
  • The need for more robust VQA models that can effectively discern and utilize relevant image information is critical.

Purpose of the Study:

  • To propose a novel VQA model that enhances accuracy by integrating external image features and a bidirectional attention mechanism.
  • To address the shortcomings of existing models in eliminating irrelevant image features and mining essential image content.
  • To improve the VQA model's ability to focus on pertinent image regions and textual cues for accurate answer prediction.

Main Methods:

  • Developed a VQA model incorporating adversarial learning with external image features to create a robust mechanism against irrelevant information.
  • Implemented a bidirectional attention mechanism to refine feature extraction and fusion, promoting focused attention and reducing interference.
  • Utilized target detection for an image-oriented attention mechanism to enhance the model's focus on relevant image components.

Main Results:

  • The proposed VQA model demonstrated superior performance compared to existing attention-based methods on standard benchmark datasets.
  • Adversarial learning effectively boosted model accuracy by leveraging external image features to refine attention.
  • Qualitative analysis of attention maps confirmed the model's ability to focus on relevant image areas for correct predictions.

Conclusions:

  • The novel VQA model effectively overcomes limitations of previous attention mechanisms by integrating adversarial learning and bidirectional attention.
  • The proposed approach significantly improves VQA accuracy and robustness by filtering irrelevant image features.
  • This work offers a promising direction for developing more sophisticated and accurate visual question answering systems.