Todd Hollon

Standard contrastive language-image pre-training can neglect objects in visual scenes. ItemizedCLIP forces models to learn and attend to all described items, resulting in better visual representations.

Recent visual agents can score well while using image tools unfaithfully-e.g., cropping irrelevant regions or ignoring tool outputs. CodeV represents tools as executable Python code and trains with Tool-Aware Policy Optimization (TAPO), using process-level rewards on visual tool inputs and outputs to improve both accuracy and faithful tool use on search and broader multimodal benchmarks.

HiDisc is a self-supervised learning method that leverages the inherent patient-slide-patch hierarchy of biomedical microscopy to learn stronger visual representations without explicit negative mining.

OpenSRH is the first public dataset of clinical stimulated Raman histology images from brain tumor patients, released alongside benchmarks to accelerate machine learning research for intraoperative brain tumor diagnosis.