Zi Huang

LLMs can learn to reason over complex text-rich networks in a zero-shot manner using reinforcement learning alone, outperforming methods relying on supervised fine-tuning or distillation.

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.