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Found 5 papers, 3 papers with code
Asymmetric Patch Sampling for Contrastive Learning
Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning.
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).
Exploring Contextual Relationships for Cervical Abnormal Cell Detection
To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.
MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation
First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks.
ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention
The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task.
Ranked #9 on
Audio Classification
on ICBHI Respiratory Sound Database
(using extra training data)
