Search Results for author:
Found 8 papers, 7 papers with code
Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models
For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process.
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs.
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
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 clinical applications with limited annotations.
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method.
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
An evaluation of U-Net in Renal Structure Segmentation
Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications.
SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite
However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes.
