AURA: Visually Interpretable Affective Understanding via Robust Archetypes

Interpretable methods such as vision--language models (VLMs) have advanced affect analysis by aligning images with textual descriptions. However, relying on text as an intermediate proxy faces critical limitations: linguistic templates are inherently discrete, making them fundamentally incompatible with continuous valence--arousal regression, while also acting as a bottleneck for fine-grained visual nuances. Cognitive psychology suggests that human affective perception is not mediated by linguistic translation, but is grounded in perceptual resemblance to internalized visual archetypes. Motivated by this, we propose AURA, an archetype-based framework that replaces brittle linguistic proxies with a self-organizing archetype manifold. By adaptively allocating representational density according to affective complexity, AURA enables accurate continuous regression and reshapes affective taxonomies by decomposing labels into interpretable, geometrically coherent visual primitives. This paradigm offers a transparent, visually grounded decision trail and achieves state-of-the-art results across discrete and continuous tasks.