Publications

Lavender efficiently fine-tunes advanced vision-language models by aligning their text-vision attention with Stable Diffusion.

We introduce Multi-Concept Prompt Learning (MCPL), an innovative approach in textural inversion, focusing on mask-free learning multiple object-level concepts simultaneously from a single sentence-image pair.

We propose Local Diffusion, a training-free framework that segments and fuses in-distribution and OOD regions, cutting structural hallucinations by 40% in medical and 25% in natural images.

We introduce a learned, adaptive downsampling method that prioritizes challenging regions, enabling efficient segmentation of high-res images on limited computing.

We introduce a dual-CNN method that learns both annotator reliability and the true segmentation labels from noisy expert annotations, overcoming biases that hinder segmentation performance.

We introduce a foveation module that dynamically adjusts patch FoV and resolution for ultra-high resolution image segmentation, achieving state-of-the-art results and significant accuracy boosts on challenging datasets.