Yadan Luo

Text-based prototypes in vision-language models are fundamentally misaligned with visual data for out-of-distribution detection, but this can be overcome with a novel online pseudo-supervised approach.

Decomposing holistic visual cues into subtle, spatially-associated discrepancies allows for state-of-the-art ultra-fine-grained classification even with limited training data.

Soybean leaves have intricate vein structures that unlock state-of-the-art ultra-fine-grained visual categorization, even with limited data.

Achieve SOTA wheat disease segmentation with limited data by cleverly combining the semantic power of DINOv2 with the geometric precision of SAM.