12 papers
HyperET: Efficient Training in Hyperbolic Space for Multi-modal Large Language Models
Zelin Peng, Zhengqin Xu, Qingyang Liu +2
Multi-modal large language models (MLLMs) have emerged as a transformative approach for aligning visual and textual understanding. They typically require extremely high computation…
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models
Yu Huang, Zelin Peng, Yichen Zhao +3
Medical image segmentation is crucial for clinical diagnosis, yet existing models are limited by their reliance on explicit human instructions and lack the active reasoning capabil…
Weight Spectra Induced Efficient Model Adaptation
Chongjie Si, Xuankun Yang, Muqing Liu +5
Large-scale foundation models have demonstrated remarkable versatility across a wide range of downstream tasks. However, fully fine-tuning these models incurs prohibitive computati…
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation
Chongjie Si, Zhiyi Shi, Yadao Wang +3
The rapid development of large language models has revolutionized natural language processing, but their fine-tuning remains computationally expensive, hindering broad deployment.…
Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
Chongjie Si, Yidan Cui, Fuchao Yang +2
Partial Multi-Label Learning (PML) extends the multi-label learning paradigm to scenarios where each sample is associated with a candidate label set containing both ground-truth la…
Why Can Accurate Models Be Learned from Inaccurate Annotations?
Chongjie Si, Yidan Cui, Fuchao Yang +2
Learning from inaccurate annotations has gained significant attention due to the high cost of precise labeling. However, despite the presence of erroneous labels, models trained on…