4 citations · 4 across the 4 of their papers we have counts for
4 papers
Qwen3-VL-Seg: Unlocking Open-World Referring Segmentation with Vision-Language Grounding
Yuan Yao, Qiushi Yang, Humen Zhong +5
Open-world referring segmentation requires grounding unconstrained language expressions to precise pixel-level regions. Existing multimodal large language models (MLLMs) exhibit st…
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning
Meilu Zhu, Qiushi Yang, Zhifan Gao +2
Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model colla…
Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuning
Yupei Zhang, Li Pan, Qiushi Yang +2
Medical multi-modal pre-training has revealed promise in computer-aided diagnosis by leveraging large-scale unlabeled datasets. However, existing methods based on masked autoencode…
Towards Robust Adaptive Object Detection under Noisy Annotations
Xinyu Liu, Wuyang Li, Qiushi Yang +2
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate th…