SSNP: support sets are natural prompts for in-context few-shot image recognition

Making Large Vision–Language Models Better Few-Shot Learners

Few-shot classification (FSC) aims to emulate the human ability to rapidly learn new concepts from a handful of examples. Large Vision-Language Models (LVLMs), with their rich prior knowledge and powerful visio-linguistic understanding capabilities, are emerging as a highly promising paradigm for FSC. This paper invest...

Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning

This paper proposes Unsupervised Transfer Clustering with Selective Querying (UTC+SQ), a framework that enhances a recent APL approach by leveraging state-of-the-art unsupervised transfer model, which generates high-fidelity pseudo-labels that establish semantically meaningful clusters, allowing for the selection of mo...

Fine-Tuned Transformers vs. LLM Prompting for Social Support Detection: A Comparative Study

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

ProCAP is proposed, 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...

CASR: Correlation-Aware Support Resampling for Few-Shot Learning with Prototypical Networks

The quality of support sets plays a critical role in few-shot learning. How well class prototypes represent their corresponding categories depends directly on the samples selected to construct these sets during episodic training. In standard implementations of Prototypical Networks, support samples are typically select...

Query-Conditioned Prototype Adaptation for Cross-Domain Few-Shot Learning: Single-Query Inference, Controlled Comparisons, and Failure Modes

Overall, WIPT provides a streaming-compatible form of test-time prototype adaptation that can improve difficult low-shot cross-domain decisions without target-time optimization.