Discovering and Mitigating Visual Biases through Keyword Explanation

Understand

Addressing biases in computer vision models is crucial for real-world AI deployments.

• However, mitigating visual biases is challenging due to their unexplainable nature, often identified indirectly through visualization or sample statistics, which necessitates additional human supervision for interpretation.
• To tackle this issue, we propose the Bias-to-Text (B2T) framework, which interprets visual biases as keywords.
• Specifically, we extract common keywords from the captions of mispredicted images to identify potential biases in the model.