[Submitted on 24 Sep 2026]
Title:ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models
View a PDF of the paper titled ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models, by Hiwa Azeez Abbas and 2 other authors
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Abstract:Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the test distribution shifts. Prompt learning adapts only a small set of parameters while keeping the backbone frozen, yet many existing multimodal prompt learners couple the visual and textual branches weakly and can be brittle in low-shot regimes. We propose ProCAP, a probabilistic cross-attentive prompt learning framework that improves cross-modal interaction and training stability without updating any CLIP weights: it learns both visual and textual prompt tokens and links them through stacked bidirectional multi-head cross-attention so the two branches refine each other across prompt depth. To reduce overfitting under limited supervision, we parameterize prompt tokens with Gaussian means and variances and regularize them with lightweight KL and L2 penalties, and we further add a compact symmetric InfoNCE head that aligns cross-attended image features with class-level text representations in a shared low-dimensional space. Across few-shot base-to-novel generalization on 11 datasets, cross-dataset transfer, and domain generalization on ImageNet shift benchmarks, ProCAP achieves strong aggregate base-to-novel performance and competitive transfer performance while keeping the CLIP backbone unchanged.
Comments: 20 pages, 8 figures, 9 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.30434 [cs.CV]
(or arXiv:2609.30434v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.30434
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hiwa Abbas [view email] [v1] Thu, 24 Sep 2026 18:31:57 UTC (5,397 KB)
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