16 citations · 66 across the 38 of their papers we have counts for
7 papers · 1 filter
Revisiting Cross-Attention Mechanisms: Leveraging Beneficial Noise for Domain-Adaptive Learning
Zelin Zang, Yehui Yang, Fei Wang +2
Unsupervised Domain Adaptation (UDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain but often suffers from severe domain and scale gaps that…
SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos
Jinlin Wu, Felix Holm, Chuxi Chen +18
While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that waste model capacity on low-level…
Anatomy-R1: Enhancing Anatomy Reasoning in Multimodal Large Language Models via Anatomical Similarity Curriculum and Group Diversity Augmentation
Ziyang Song, Zelin Zang, Zuyao Chen +6
Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored, especially in c…
NeuroABench: A Multimodal Evaluation Benchmark for Neurosurgical Anatomy Identification
Ziyang Song, Zelin Zang, Xiaofan Ye +7
Multimodal Large Language Models (MLLMs) have shown significant potential in surgical video understanding. With improved zero-shot performance and more effective human-machine inte…
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier
Zelin Zang, Lei Shang, Senqiao Yang +4
Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the prese…
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
Siyuan Li, Di Wu, Fang Wu +2
Masked image modeling, an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers. Its underlyin…
Exploring Localization for Self-supervised Fine-grained Contrastive Learning
Di Wu, Siyuan Li, Zelin Zang +1
Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classifica…