Low-cost concept-based localized explanations: How far can we get with training-free approaches?
For explainable AI researchers, this work provides a low-cost, training-free approach to generate concept-based explanations, though results are incremental as they apply existing MLLMs to a known bottleneck.
The paper evaluates whether mid-scale Multimodal Large Language Models (MLLMs) can perform localized concept naming under zero-shot conditions, achieving 62%-88% object-level exact-match accuracy, demonstrating the potential of training-free concept annotation.
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 consistent performance trends across datasets, reaching 62%-88% object-level exact-match accuracy, highlighting the potential of training-free concept annotation from localized regions. We discuss limitations and failure modes and release a reproducible framework to support future low-cost C-XAI research.