PMCE: Probabilistic Multi-Granularity Semantics with Caption-Guided Enhancement for Few-Shot Learning

• URL: http://arxiv.org/abs/2601.14111v1
• Date: Tue, 20 Jan 2026 16:06:23 GMT
• Title: PMCE: Probabilistic Multi-Granularity Semantics with Caption-Guided Enhancement for Few-Shot Learning
• Authors: Jiaying Wu, Can Gao, Jinglu Hu, Hui Li, Xiaofeng Cao, Jingcai Guo,
• Abstract summary: Few-shot learning aims to identify novel categories from only a handful of labeled samples.<n>We present PMCE, a framework that leverages Multi-granularity semantics with Caption-guided Enhancement.
• Score: 32.31794815671946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning aims to identify novel categories from only a handful of labeled samples, where prototypes estimated from scarce data are often biased and generalize poorly. Semantic-based methods alleviate this by introducing coarse class-level information, but they are mostly applied on the support side, leaving query representations unchanged. In this paper, we present PMCE, a Probabilistic few-shot framework that leverages Multi-granularity semantics with Caption-guided Enhancement. PMCE constructs a nonparametric knowledge bank that stores visual statistics for each category as well as CLIP-encoded class name embeddings of the base classes. At meta-test time, the most relevant base classes are retrieved based on the similarities of class name embeddings for each novel category. These statistics are then aggregated into category-specific prior information and fused with the support set prototypes via a simple MAP update. Simultaneously, a frozen BLIP captioner provides label-free instance-level image descriptions, and a lightweight enhancer trained on base classes optimizes both support prototypes and query features under an inductive protocol with a consistency regularization to stabilize noisy captions. Experiments on four benchmarks show that PMCE consistently improves over strong baselines, achieving up to 7.71% absolute gain over the strongest semantic competitor on MiniImageNet in the 1-shot setting. Our code is available at https://anonymous.4open.science/r/PMCE-275D

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