5 papers
Perceptual Taxonomy: Evaluating and Guiding Hierarchical Scene Reasoning in Vision-Language Models
Jonathan Lee, Xingrui Wang, Jiawei Peng +9
We propose Perceptual Taxonomy, a structured process of scene understanding that first recognizes objects and their spatial configurations, then infers task-relevant properties suc…
Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models
Tiezheng Zhang, Yitong Li, Yu-cheng Chou +4
Building state-of-the-art Vision-Language Models (VLMs) with strong captioning capabilities typically necessitates training on billions of high-quality image-text pairs, requiring…
SpatialReasoner: Towards Explicit and Generalizable 3D Spatial Reasoning
Wufei Ma, Yu-Cheng Chou, Qihao Liu +4
Despite recent advances on multi-modal models, 3D spatial reasoning remains a challenging task for state-of-the-art open-source and proprietary models. Recent studies explore data-…
Expectation-Maximization as the Engine of Scalable Medical Intelligence
Wenxuan Li, Pedro R. A. S. Bassi, Tianyu Lin +19
Large, high-quality, annotated datasets are the foundation of medical AI research, but constructing even a small, moderate-quality, annotated dataset can take years of effort from…
Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
Yichen Wang, Yuxuan Chou, Ziqi Zhou +4
As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations t…