Search Results for author:
Found 6 papers, 4 papers with code
Generate Any Scene: Evaluating and Improving Text-to-Vision Generation with Scene Graph Programming
We introduce Generate Any Scene, a framework that systematically enumerates scene graphs representing a vast array of visual scenes, spanning realistic to imaginative compositions.
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Our multi-image instruction data leads to an 8% improvement on Mantis-Eval.
Ranked #107 on
Visual Question Answering
on MM-Vet
m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks
With m&m's, we evaluate 10 popular LLMs with 2 planning strategies (multi-step vs. step-by-step planning), 2 plan formats (JSON vs. code), and 3 types of feedback (parsing/verification/execution).
Network analysis on cortical morphometry in first-episode schizophrenia
Our work supports that cortical morphological connectivity, which is constructed based on correlations across subjects' cortical thickness, may serve as a tool to study topological abnormalities in neurological disorders.
LesionPaste: One-Shot Anomaly Detection for Medical Images
Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training.
