10 citations · 15 across the 9 of their papers we have counts for
9 papers
MOSAIC: Adaptive Inter-layer Composition for Efficient Heterogeneous Vision-Language Models
Yuncheng Yang, Feiyang Ye, Shixian Luo +7
Vision-Language Models (VLMs) have achieved success using homogeneous Transformers to process multimedia data. Recent studies show that heterogeneous structures interleaving effici…
GeoGramBench: Benchmarking the Geometric Program Reasoning in Modern LLMs
Shixian Luo, Zezhou Zhu, Yu Yuan +3
Geometric spatial reasoning forms the foundation of many applications in artificial intelligence, yet the ability of large language models (LLMs) to operate over geometric spatial…
Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models
Yuncheng Yang, Yulei Qin, Tong Wu +9
The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stabl…
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Yulei Qin, Yuncheng Yang, Pengcheng Guo +7
Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LL…
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Yuncheng Yang, Chuyan Zhang, Zuopeng Yang +6
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently opt…
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Chuyan Zhang, Yuncheng Yang, Hao Zheng +1
Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinica…
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
Yuncheng Yang, Meng Wei, Junjun He +3
Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance…
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
Jin Ye, Haoyu Wang, Ziyan Huang +8
Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely…
An evaluation of U-Net in Renal Structure Segmentation
Haoyu Wang, Ziyan Huang, Jin Ye +7
Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications. Kidney PArsing~(KiPA 2022) Chal…