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Applied Research

We introduce a new nationwide dataset and a scalable zero-shot workflow for building attribute extraction using large vision-language models. Extensive benchmarking shows our approach significantly outperforms existing baselines and supervised methods.

Annotating every aerial image available for training a classifier is unnecessary. Our algorithm selects a small subset of images that are diverse yet representative for labeling, leading to an effective semi-supervised classifier using only 6.4% labeled data in our case study.

Foveal Avascular Zone (FAZ) segmentation can be improved by incorporating auxiliary tasks like FAZ boundary segmentation and using weak supervision from vessel segmentation. This helps the model learn finer details and contextual cues, leading to SOTA results.

We present iSparseUnet, a segmentation model for 3D Computed Tomography (CT) images that leverages data sparsity, octree structure, and invertible layers for optimal efficiency. Unlike patch-based methods, it processes the entire volume at once, producing hierarchical outputs that preserve global context and maintain the connectivity of lung structures, essential for precise medical segmentation.