AI

ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution

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Briefings

ConEx introduces a framework linking saliency visualization to concept-based reasoning in vision models. It automatically discovers class-specific concepts using concept activation vectors (CAVs) learned via architecture-specific masking, which reduces segmentation mask noise. The method generates faithful saliency maps showing concept locations and contributions, evaluated by Vector-Concept Match (VCM) and Concept-Class Match (CCM) metrics. Experiments show ConEx achieves state-of-the-art performance on faithfulness, segmentation, and concept-quality benchmarks across diverse settings.

Key points

• ConEx automatically discovers class-specific concepts using concept activation vectors (CAVs) learned without manual supervision.
• The framework uses an architecture-specific masking mechanism to reduce noise from segmentation masks, enhancing concept purity.

Context

Visual explanation methods in computer vision often highlight pixel importance but fail to link these cues to semantically meaningful concepts, limiting interpretability. ConEx bridges this gap by integrating concept-based reasoning with saliency visualization to improve trustworthiness.

Limitations

The study evaluates ConEx across diverse settings but does not report results on specific hardware configurations or provide a timeline for future deployment in production environments.

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