3 citations · 4 across the 9 of their papers we have counts for
10 papers
Open-Ended CT Volume Segmentation with Weak Supervision from Language
Sanjay Subramanian, Junwei Yu, Zirui Wang +5
We introduce a method for training a text-conditioned segmentation model for CT scans, which combines voxel-level supervision with coarse but scalable slice-level supervision from…
Stateful Visual Encoders for Vision-Language Models
Zirui Wang, Junwei Yu, Adam Yala +3
Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes. However, in existing open-weight VLMs, vis…
ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
Long Lian, Sida Wang, Felix Juefei-Xu +7
Scaling inference-time computation has enabled Large Language Models (LLMs) to achieve strong reasoning performance, but their inherently sequential decoding incurs substantial lat…
Pillar-0: A New Frontier for Radiology Foundation Models
Kumar Krishna Agrawal, Longchao Liu, Long Lian +11
Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full sp…
Data reuse enables cost-efficient randomized trials of medical AI models
Michael Nercessian, Wenxin Zhang, Alexander Schubert +4
Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hind…
Describe Anything: Detailed Localized Image and Video Captioning
Long Lian, Yifan Ding, Yunhao Ge +8
Generating detailed and accurate descriptions for specific regions in images and videos remains a fundamental challenge for vision-language models. We introduce the Describe Anythi…
TULIP: Towards Unified Language-Image Pretraining
Zineng Tang, Long Lian, Seun Eisape +6
Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understandin…
Learning Adaptive Parallel Reasoning with Language Models
Jiayi Pan, Xiuyu Li, Long Lian +6
Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chai…
Atlas: Multi-Scale Attention Improves Long Context Image Modeling
Kumar Krishna Agrawal, Long Lian, Longchao Liu +6
Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-…
Rethinking Patch Dependence for Masked Autoencoders
Letian Fu, Long Lian, Renhao Wang +6
In this work, we examine the impact of inter-patch dependencies in the decoder of masked autoencoders (MAE) on representation learning. We decompose the decoding mechanism for mask…