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Found 11 papers, 8 papers with code
CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection
We propose Class-Aware Teacher (CAT) to address the class bias issue in the domain adaptation setting.
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing
Self-supervised frameworks for representation learning have recently stirred up interest among the remote sensing community, given their potential to mitigate the high labeling costs associated with curating large satellite image datasets.
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Object detection at night is a challenging problem due to the absence of night image annotations.
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especially in clinical practice.
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation.
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements.
Object-Aware Self-supervised Multi-Label Learning
Multi-label Learning on Image data has been widely exploited with deep learning models.
A predictive analytics approach for stroke prediction using machine learning and neural networks
Therefore, it is vital to study the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction.
Connection Pruning for Deep Spiking Neural Networks with On-Chip Learning
We applied our approach to a deep SNN with the Time To First Spike (TTFS) coding and has successfully achieved 2. 1x speed-up and 64% energy savings in the on-chip learning and reduced the network connectivity by 92. 83%, without incurring any accuracy loss.
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide.
Deeply Supervised Active Learning for Finger Bones Segmentation
Segmentation is a prerequisite yet challenging task for medical image analysis.
