# PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models Source: [PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:14.564Z Authors: Mayank Nautiyal, Stela Arranz Gheorghe, Kristiana Stefa, Li Ju, Ida-Maria Sintorn, Prashant Singh Published: 2025-03-14T12:53:37.000Z ## Abstract Language-guided attention frameworks have significantly enhanced both interpretability and performance in image classification; however, the reliance on deterministic embeddings from pre-trained vision-language foundation models to generate reference attention maps frequently overlooks the intrinsic multivaluedness and ill-posed characteristics of cross-modal mappings. To address these limitations, we introduce PARIC, a probabilistic framework for guiding visual attention via language specifications. Our approach enables pre-trained vision-language models to generate probabilistic reference attention maps, which align textual and visual modalities more effectively while incorporating uncertainty estimates, as compared to their deterministic counterparts. Experiments on benchmark test problems demonstrate that PARIC enhances prediction accuracy, mitigates bias, ensures consistent predictions, and improves robustness across various datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()