OpenMorphoQuery: From Visual Morphology Prompts to Target-Grounded Queries for Unseen Industrial Defect Localization

Under a category-disjoint open-set setting, localizing unseen industrial defects from scarce visual morphology prompts requires transferring fine-grained morphology revealed by exemplars to target images. Such transfer characterizes morphology-defined openness, whose central challenge stems from a reference-frame mismatch in morphological representation. Within prompt RoIs, discriminative morphology is encoded relative to material texture, product context, and annotation boundaries rather than as an independently transferable category attribute. Conventional aggregation preserves this sample-dependent anchoring, yielding prototypes that characterize the intended morphology but cannot establish reliable correspondence with its target-image manifestation. We propose OpenMorphoQuery (OMQ), a morphology-guided framework that addresses this mismatch through progressive reference-frame transformation. The Normalized Morphology Dictionary Encoder re-expresses context-bound RoI features relative to shared morphology bases through normalized residual coding, establishing a transferable morphology frame. Perturbation-Consistent Prototype Experts retain relations stable under appearance, geometry, and boundary perturbations, further stabilizing this frame against incidental variations. Prototype-Transport Query Initialization then transports the stabilized prototypes into a target-conditioned frame through optimal-transport alignment, yielding spatially grounded decoder queries. Experiments on a unified four-dataset benchmark of over 160,000 images establish state-of-the-art aggregate performance in multi-granular morphology grounding among visual-prompt methods. Ablations further quantify the incremental contribution of each component.