1 citations · 3 across the 13 of their papers we have counts for
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LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows
Xiaofeng Mao, Peijia Lin, Shaohao Rui +3
Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon scenes drift in appearance,…
BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression
Shaohao Rui, Xiaofeng Mao, Zhanyu Zhang +6
Interactive video world models commonly convert bidirectional video generators into causal autoregressive systems through control fine-tuning, autoregressive training, causal initi…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
PixelSmile: Toward Fine-Grained Facial Expression Editing
Jiabin Hua, Hengyuan Xu, Aojie Li +4
Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous a…
InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-training
Zihao Luo, Shaohao Rui, Zhenyu Tang +2
Continual self-supervised learning (CSSL) in medical imaging trains a foundation model sequentially, alleviating the need for collecting multi-modal images for joint training and o…
Think Twice to See More: Iterative Visual Reasoning in Medical VLMs
Kaitao Chen, Shaohao Rui, Yankai Jiang +6
Medical vision-language models (VLMs) excel at image-text understanding but typically rely on a single-pass reasoning that neglects localized visual cues. In clinical practice, how…
PRISM: A Framework Harnessing Unsupervised Visual Representations and Textual Prompts for Explainable MACE Survival Prediction from Cardiac Cine MRI
Haoyang Su, Jin-Yi Xiang, Shaohao Rui +6
Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for S…
CTSL: Codebook-based Temporal-Spatial Learning for Accurate Non-Contrast Cardiac Risk Prediction Using Cine MRIs
Haoyang Su, Shaohao Rui, Jinyi Xiang +2
Accurate and contrast-free Major Adverse Cardiac Events (MACE) prediction from Cine MRI sequences remains a critical challenge. Existing methods typically necessitate supervised le…
Multi-modal Vision Pre-training for Medical Image Analysis
Shaohao Rui, Lingzhi Chen, Zhenyu Tang +4
Self-supervised learning has greatly facilitated medical image analysis by suppressing the training data requirement for real-world applications. Current paradigms predominantly re…