Search Results for author:
Found 20 papers, 11 papers with code
Chinese-Vicuna: A Chinese Instruction-following Llama-based Model
Chinese-Vicuna is an open-source, resource-efficient language model designed to bridge the gap in Chinese instruction-following capabilities by fine-tuning Meta's LLaMA architecture using Low-Rank Adaptation (LoRA).
STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation
To address these issues, we propose STPNet, a Scale-aware Text Prompt Network that leverages vision-language modeling to enhance medical image segmentation.
COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation
Accurate segmentation of 3D vascular structures is essential for various medical imaging applications.
Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think
(3) With the above two-stage models excelling in motion controllability and degree, we decouple the relevant parameters associated with each type of motion ability and inject them into the base I2V-DM.
FSC-loss: A Frequency-domain Structure Consistency Learning Approach for Signal Data Recovery and Reconstruction
A core challenge for signal data recovery is to model the distribution of signal matrix (SM) data based on measured low-quality data in biomedical engineering of magnetic particle imaging (MPI).
Large Language Models for Manufacturing
The rapid advances in Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation.
An Analysis for Image-to-Image Translation and Style Transfer
Finally, we conclude that image-to-image translation divides images by domain, and the types of images in the domain are limited, and the scope involved is small, but the conversion ability is strong and can achieve strong semantic changes.
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
\thm{Can we fine-tune a series of task-specific small models and transfer their knowledge directly to a much larger model without additional training?}
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models
Our approach is grounded in the empirical observation that pairwise comparisons can effectively alleviate boundary ambiguity and inherent bias.
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method
Gaze plays a crucial role in revealing human attention and intention, particularly in hand-object interaction scenarios, where it guides and synchronizes complex tasks that require precise coordination between the brain, hand, and object.
Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding
This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics.
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival Analysis
However, ranking loss only focus on the ranking of survival time and does not consider potential effect of samples for exact survival time values.
Benefit from public unlabeled data: A Frangi filtering-based pretraining network for 3D cerebrovascular segmentation
In this study, we construct the largest preprocessed unlabeled TOF-MRA datasets (1510 subjects) to date.
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
Furthermore, we demonstrate that our model can generalize to two unseen sites with 229 WSIs.
Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19.
Ranked #1 on
Medical Image Segmentation
on MosMedData
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks.
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles
Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks.
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Medical image segmentation is one of the most fundamental tasks concerning medical information analysis.
Ranked #5 on
Semantic Segmentation
on ACDC Scribbles
Speckle-based optical cryptosystem and its application for human face recognition via deep learning
A scattering ground glass is exploited to generate physical secret keys of gigabit length and encrypt face images via seemingly random optical speckles at light speed.
A Discrete-event-based Simulator for Distributed Deep Learning
New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way.
