From Localization to Quantification: Structure-Guided Attribute Prediction under Weak Supervision

Quantitative microstructural analysis often relies on instance segmentation to estimate material attributes. However, dense instance annotations are costly to obtain, whereas image-level attribute labels are widely available in the literature and technical reports. This supervision gap hinders the effective use of abundant attribute labels while preserving essential structural information. To address this challenge, we propose Structure-Guided Attribute Prediction (SGAP), a two-stage framework that leverages scarce structural annotations alongside abundant image-level attribute labels. In the first stage, SGAP learns structural representations from a limited set of instance masks. In the second stage, a frozen structural pathway provides localization priors, while a LoRA-adapted attribute pathway extracts task-specific features for attribute prediction. SGAP uses segmentation-derived spatial priors to guide feature aggregation and predicts attributes through learned heads, without explicit per-instance geometric measurement. Experiments across two distinct scenarios and domains show that SGAP consistently outperforms both segment-then-measure approaches and direct prediction baselines, achieving state-of-the-art performance. These results support the use of segmentation-derived localization priors for attribute learning under heterogeneous supervision.