Robust visual question answering via polarity enhancement and contrast
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an unbiased Visual Question Answering (VQA) method to overcome language biases. The novel approach enhances model performance on VQA datasets by focusing on answer-image correlations.
Area Of Science
- Artificial Intelligence
- Computer Vision
- Natural Language Processing
Background
- Visual Question Answering (VQA) models often rely on question-answer correlations, neglecting visual content.
- This reliance on language priors weakens the model's ability to truly understand image-text relationships.
Purpose Of The Study
- To propose an unbiased VQA method that mitigates language priors.
- To enhance the correlation between visual content and textual information in VQA models.
Main Methods
- A novel two-module model architecture was designed.
- The Answer Visual Attention Modules generate positive predictions, while the Dual Channels Joint Module generates negative predictions.
- A new loss function was developed to train the model using positive and negative predictions alongside the correct answer.
Main Results
- The proposed method achieved 61.24% performance on the VQA-CP v2 dataset.
- Unlike existing debiasing methods, this approach improved performance on both VQA v2 and VQA-CP v2 datasets without performance degradation on the VQA v2 dataset.
Conclusions
- The developed unbiased VQA method effectively addresses language priors.
- The model demonstrates improved performance across multiple VQA benchmarks, highlighting its robustness and generalizability.
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