8 papers
When Can Test-Time Adaptation Help Zero-Shot CT Vision-Language Models?
Ailar Mahdizadeh, Puria Azadi Moghadam, Xiangteng He +1
3D CT vision-language models (VLMs) classify abnormalities from text prompts in a zero-shot manner, enabling cross-institution deployment where labels are scarce and clinical tasks…
Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs
Shayan Mohammadizadehsamakosh, Pritam Sarkar, Leonid Sigal +2
Large Vision-Language Models (LVLMs) have achieved strong performance across medical imaging tasks, yet they remain prone to factual inconsistencies, poor visual grounding, and mis…
DSeq-JEPA: Discriminative Sequential Joint-Embedding Predictive Architecture
Xiangteng He, Shunsuke Sakai, Shivam Chandhok +5
Recent advances in self-supervised visual representation learning have demonstrated the effectiveness of predictive latent-space objectives for learning transferable features. In p…
InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models
Shunsuke Sakai, Xiangteng He, Chunzhi Gu +2
Despite the remarkable success, recent reconstruction-based anomaly detection (AD) methods via diffusion modeling still involve fine-grained noise-strength tuning and computational…
SCALE-VLP: Soft-Weighted Contrastive Volumetric Vision-Language Pre-training with Spatial-Knowledge Semantics
Ailar Mahdizadeh, Puria Azadi Moghadam, Xiangteng He +3
Vision-language models (VLMs) have demonstrated strong cross-modal capabilities, yet most work remains limited to 2D data and assumes binary supervision (i.e., positive vs. negativ…
To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models
Jiayun Luo, Wan-Cyuan Fan, Lyuyang Wang +4
Large Vision Language Models (LVLMs) have recently emerged as powerful architectures capable of understanding and reasoning over both visual and textual information. These models t…