7 citations · 7 across the 5 of their papers we have counts for
6 papers
Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models
Ke Hao, Yuanzhi Liang, Tingxi Chen +5
Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Gen…
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
Rui Li, Ke Hao, Yuanzhi Liang +4
Reinforcement learning, particularly Group Relative Policy Optimization (GRPO), has emerged as an effective framework for post-training visual generative models with human preferen…
Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes
Chuyan Zhang, Kefan Wang, Yun Gu
Low-Rank Adaptation (LoRA) has proven effective in reducing computational costs while maintaining performance comparable to fully fine-tuned foundation models across various tasks.…
PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation
Chuyan Zhang, Hao Zheng, Xin You +2
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training…
Reflecting Topology Consistency and Abnormality via Learnable Attentions for Airway Labeling
Chenyu Li, Minghui Zhang, Chuyan Zhang +1
Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway anatomical labeling is cha…
Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks
Chuyan Zhang, Yun Gu
Self-supervised learning (SSL) has achieved remarkable performance in various medical imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific do…