Low-cost concept-based localized explanations: How far can we get with training-free approaches?
Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations. We evaluate whether mid-scale Multimodal Large Language Models (MLLMs) can perform localized concept naming under strict zero-shot conditions by assigning labels to bounding-box regions at both object and part levels. We propose a reproducible zero-shot evaluation protocol for Concept Naming (CoNa) with (i) closed-set, category-constrained prompting for moderate vocabularies and (ii) Open-CoNa, an embedding-similarity-based strategy for large label spaces. Experiments with four MLLMs (7B-32B) show consist...
| Authors | Darian Fernández-Gutiérrez, Rafael Bello, Marilyn Bello et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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