Learning Manipulation Features for Quantitative Assessment and Skill-Level Classification in Robot-Assisted Intervention: In Vivo Rabbit Studies [0.03%]
用于机器人辅助介入的量化评估和技能水平分类的操纵特征学习：活兔研究
Siyi Wei,Zhiwei Wu,Jiahao Luo et al.
Siyi Wei et al.
Robot-assisted vascular interventions demand precise manipulation within tortuous millimeter-scale vessels, where surgical outcomes remain critically dependent on surgeon skill. Conventional metrics such as completion time or navigation suc...
Human-Like Multimodal Fake News Detection via Reflective Summarization and Large-Small Model Collaboration [0.03%]
基于反射摘要和大小模型协作的人类级多模态虚假新闻检测方法
Boyue Wang,Yihan Gao,Tengfei Liu et al.
Boyue Wang et al.
While multimodal fake news detection methods have made progress in aligning multimodal semantics, they still face significant challenges in analyzing background context, emotional tone, and the overall plausibility of news content. To addre...
An End-to-End Signal-Level Framework for Multifunction Radar Working Mode Recognition [0.03%]
多功能雷达工作模式识别的端到端信号级框架
Yuming Liu,Guolong Cui,Mou Wang et al.
Yuming Liu et al.
Multifunction radar (MFR) systems dynamically switch among diverse working modes according to mission requirements and environmental conditions. Accurate, real-time recognition of these modes is essential for radar intelligence and electron...
Approximate Optimal Control for Morphing Aircraft via Attention Meta-Learning and Continual Learning [0.03%]
基于注意力元学习和连续学习的变体飞机近似最优控制
Hao-Chi Che,Huai-Ning Wu
Hao-Chi Che
This study presents an innovative approximate optimal controller for variable-span morphing aircraft (MA), where the mapping relationship between aerodynamic parameters and deformation parameters is unknown. The foundation of our approach r...
Collaborative Hyperparameter Recommendation by Coupled Matrix Factorization With Kernel [0.03%]
基于核函数耦合矩阵分解的协作超参数推荐
Liping Deng,Mingqing Xiao
Liping Deng
Hyperparameters significantly influence the learning process and performance of machine learning algorithms, rendering their effective selection a critical challenge. Meta-learning-based hyperparameter recommendation has shown promise, yet ...
Infrared and Visible Image Fusion With Language-Driven Loss and Knowledge Distillation [0.03%]
基于语言驱动损失和知识蒸馏的红外与可见光图像融合方法
Yuhao Wang,Lingjuan Miao,Zhiqiang Zhou et al.
Yuhao Wang et al.
Infrared and visible image fusion (IVIF) has attracted much attention owing to the highly complementary properties of the two image modalities. Due to the lack of ground-truth fused images, the fusion output of current deep-learning-based m...
PteFBIC: Exploiting Pterylotic Relationship for Fine-Grained Bird Image Classification via Rachidian Orientation Learning [0.03%]
基于羽毛排列关系的细粒度鸟类图像分类方法研究
Hai Liu,Song He,Tingting Liu et al.
Hai Liu et al.
Fine-grained bird image classification (FBIC) is crucial for ecological monitoring and biodiversity conservation, yet it remains challenging under camouflaged appearances, body occlusions, and arbitrary postures. To address these issues, we...
ACTFormer: Adaptive Complexity-Aware Traffic Transformer for Intelligent Flow Prediction [0.03%]
自适应复杂度感知交通变换器的智能流预测方法
Wenbiao Yang,Wenli Shang,Zhiquan Liu
Wenbiao Yang
Traffic time-series forecasting faces significant challenges from varying data complexity and domain-specific temporal patterns that existing transformer approaches fail to address through fixed architectural configurations. This article in...
GLRT-Based Deep Metric Learning for Robust Remote Sensing Object Retrieval [0.03%]
基于GLRT的鲁棒遥感目标检索深度度量学习方法
Linping Zhang,Xueqian Wang,Zhizhuo Jiang et al.
Linping Zhang et al.
Remote sensing object retrieval (RSOR) aims to identify images of the same object from large remote sensing image databases. However, existing RSOR methods often rely on simple and distribution-agnostic distance metrics, for example, Euclid...
Young Woon Cho,Sungmin Lee,Sangbum Kim
Young Woon Cho
Analog in-memory computing (AIMC) is a promising technology for energy-efficient acceleration of deep learning workloads. While significant advancements have been achieved in accelerating on-chip inference, on-chip training has not received...