Counterfactual causal inference for robust visual question answering.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Visual Question Answering (VQA) systems leverage multimodal data but suffer from inherent language and vision biases, limiting generalization.
• Existing VQA models often fail to generalize due to spurious correlations between modalities.

Purpose of the Study:

• To introduce a novel counterfactual causal framework (CC-VQA) for mitigating cross-modality biases in VQA.
• To enhance the accuracy, robustness, and generalization capabilities of VQA models.

Main Methods:

• Developed a counterfactual causal framework (CC-VQA) employing Counterfactual Sample Synthesis (CSS) and causal inference.
• Utilized causal graphs to disentangle spurious correlations within multimodal data.
• Proposed a contrastive loss mechanism and a robust training strategy to improve model performance.

Main Results:

• CC-VQA demonstrated substantial improvements in bias mitigation and overall accuracy on benchmark datasets (VQA-CP v2, VQA v2).
• The framework effectively disentangled spurious correlations, leading to more balanced and precise multimodal reasoning.
• Outperformed state-of-the-art methods in enhancing VQA system performance.

Conclusions:

• The proposed CC-VQA framework offers an effective solution for addressing biases in VQA systems.
• CC-VQA significantly improves the generalization and accuracy of VQA models through causal inference and robust training.
• This work advances the field of multimodal AI by providing a method for unbiased and robust visual question answering.