Search Results for author:
Found 8 papers, 5 papers with code
Implicit In-context Learning
In-context Learning (ICL) empowers large language models (LLMs) to adapt to unseen tasks during inference by prefixing a few demonstration examples prior to test queries.
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments
This paper introduces GAgent: an Gripping Agent designed for open-world environments that provides advanced cognitive abilities via VLM agents and flexible grasping abilities with variable stiffness soft grippers.
DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image
Accurate 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics.
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Inspired by the training program of medical radiology residents, we propose a shift towards universal medical image segmentation, a paradigm aiming to build medical image understanding foundation models by leveraging the diversity and commonality across clinical targets, body regions, and imaging modalities.
Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning
In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting.
Towards Self-supervised and Weight-preserving Neural Architecture Search
In this work, we propose the self-supervised and weight-preserving neural architecture search (SSWP-NAS) as an extension of the current NAS framework by allowing the self-supervision and retaining the concomitant weights discovered during the search stage.
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique.
