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arXiv · 2610.34846

Seeing Before Naming: Context-Grounded Learning of Novel Visual Concepts

Gabriel Adineera·Jinoh Kim
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Abstract

Learning an unfamiliar visual category requires more than adapting a classifier: the model must connect its visual evidence to a useful semantic name. A pre-trained vision-language model can retain discriminative image features while providing a weak textual anchor for the target concept. We address this imbalance through context-grounded semantic anchoring. Our framework, SynerNet, learns concept-specific name embeddings, exposes them to compatible linguistic contexts, and conditions their representations on visual evidence. Difficulty-aware processing and adaptive objective weighting coordinate this adaptation while retaining the pre-trained backbone. On four VISTA-Beyond domains, SynerNet reaches a mean 16-shot accuracy of 74.35%, compared with 72.45% for few-shot name learning. Across 14 domains in the name-masked transfer setting, it improves mean accuracy from 45.17% to 46.74%. Ablations identify concept-specific name embeddings and cross-modal context integration as important components. These results support treating concept acquisition as the construction of visually grounded semantic anchors.

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Gabriel Adineera, Jinoh Kim. 2026-10-26. Seeing Before Naming: Context-Grounded Learning of Novel Visual Concepts. https://arxiv.org/abs/2610.34846

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