1 citations · 2 across the 8 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.CV2026
What Makes Synthetic Data Effective in Image Segmentation
Jinjin Zhang, Xiefan Guo, Yizhou Jin +2
Driven by rapid advances in large-scale generative models, synthetic data has emerged as a promising solution for visual understanding. While modern diffusion models achieve remark…
cs.CV2026
CoVFT: Context-aware Visual Fine-tuning for Multimodal Large Language Models
Nan Zhou, Huiqun Wang, Yaoyan Zheng +1
Multimodal large language models (MLLMs) achieve remarkable progress in cross-modal perception and reasoning, yet a fundamental question remains unresolved: should the vision encod…
cs.CV2026
A multimodal vision foundation model for generalizable knee pathology
Kang Yu, Dingyu Wang, Zimu Yuan +8
Musculoskeletal disorders represent a leading cause of global disability, creating an urgent demand for precise interpretation of medical imaging. Current artificial intelligence (…