# DisParQ

DisParQ learns spatially grounded, discrete concept representations from a powerful frozen vision-only self-supervised backbone without class labels or language supervision: each image patch is assigned to exactly one concept from a learnable prototype dictionary, only a sparse subset of concepts may activate per image, and continuous residuals quantized into discrete attributes capture how each concept varies; a spatial decoder reconstructs the backbone's representation from concepts and attributes alone. Across seven datasets it reaches 83.2% top-1 on ImageNet linear probing, closely matching its frozen DINOv2 teacher, achieves higher concept consistency than language-aligned models, remains competitive on fine-grained recognition, and enables cross-category part-based retrieval.

DisParQ learns spatially grounded, discrete concept representations from a powerful frozen vision-only self-supervised backbone without class labels or language supervision: each image patch is assigned to exactly one concept from a learnable prototype dictionary, only a sparse subset of concepts may activate per image, and continuous residuals quantized into discrete attributes capture how each concept varies; a spatial decoder reconstructs the backbone's representation from concepts and attributes alone. Across seven datasets it reaches 83.2% top-1 on ImageNet linear probing, closely matching its frozen DINOv2 teacher, achieves higher concept consistency than language-aligned models, remains competitive on fine-grained recognition, and enables cross-category part-based retrieval.