# Vision-language alignment

Liver CT diagnosis models usually predict disease labels directly from images, so they only recognize diseases seen in training. DbC splits diagnosis into two steps: it learns a set of radiological findings as dense maps over the scan, then defines each disease by the findings its guidelines require and composes the diagnosis map with a fixed, parameter-free soft-minimum operator. On multi-phase liver CT, for six diseases never seen in training, DbC localized lesions significantly better than every comparator, including models that align images with disease names, and kept its advantage on rare diseases from another institution and on a public external cohort.

Liver CT diagnosis models usually predict disease labels directly from images, so they only recognize diseases seen in training. DbC splits diagnosis into two steps: it learns a set of radiological findings as dense maps over the scan, then defines each disease by the findings its guidelines require and composes the diagnosis map with a fixed, parameter-free soft-minimum operator. On multi-phase liver CT, for six diseases never seen in training, DbC localized lesions significantly better than every comparator, including models that align images with disease names, and kept its advantage on rare diseases from another institution and on a public external cohort.

Gait attribute recognition now has a large multimodal benchmark: RA-GAR covers 1,001 subjects and 242,729 walking sequences annotated with 15 static and motion gait attributes, addressing the shortage of fine-grained annotation. The accompanying Gait-VL matches attribute-specific multi-frame visual evidence against textual class prototypes instead of representing candidate classes through learned classifier output weights. It reaches state-of-the-art attribute recognition on RA-GAR, and replacing generic upstream vision models with the attribute-pretrained encoder yields consistent gains across several identity recognition pipelines and benchmarks.

Gait attribute recognition now has a large multimodal benchmark: RA-GAR covers 1,001 subjects and 242,729 walking sequences annotated with 15 static and motion gait attributes, addressing the shortage of fine-grained annotation. The accompanying Gait-VL matches attribute-specific multi-frame visual evidence against textual class prototypes instead of representing candidate classes through learned classifier output weights. It reaches state-of-the-art attribute recognition on RA-GAR, and replacing generic upstream vision models with the attribute-pretrained encoder yields consistent gains across several identity recognition pipelines and benchmarks.