Results 51 to 60 of about 141,868 (281)
Human Attribute Recognition— A Comprehensive Survey
Applied Sciences, 2020 Human Attribute Recognition (HAR) is a highly active research field in computer vision and pattern recognition domains with various applications such as surveillance or fashion. Several approaches have been proposed to tackle the particular challenges in
Ehsan Yaghoubi +5 more
doaj +1 more source
Why human connection is the true metric of research success
FEBS Open Bio, EarlyView.Human‐centred mentorship can be shaped by mentor attributes, actions, intrinsic drive and career ambition. Drawing on reflections across Singapore and France, as well as workshop insights from FEBS‐IUBMB ENABLE 2024, this article shows that human‐centred mentorship creates the conditions for sustainable growth, well‐being and retention in research ...
Timothy Lin Yun Tan +3 more
wiley +1 more source
Learning Imbalanced Data with Vision Transformers
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task. Existing LTR methods seldom train Vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons.
Zhengzhuo Xu +4 more
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi +2 more
wiley +1 more source
Offline Reinforcement Learning with Imbalanced Datasets
CoRR, 2023 The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety considerations. In this paper, we specify
Li Jiang 0008 +5 more
openaire +2 more sources
Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito +14 more
wiley +1 more source
A Class Imbalance Loss for Imbalanced Object Recognition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 The class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient.
Linbin Zhang +5 more
doaj +1 more source
IEEE Access, 2022 The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others.
Abeer S. Desuky +4 more
doaj +1 more source
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
, 2017 Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
Bacao, Fernando +2 more
core +1 more source
Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi +8 more
wiley +1 more source