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08/22/2023
Masked Cross-image Encoding for Few-shot Segmentation
Few-shot segmentation (FSS) is a dense prediction task that aims to infe...
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08/14/2023
Channel-Wise Contrastive Learning for Learning with Noisy Labels
In real-world datasets, noisy labels are pervasive. The challenge of lea...
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07/11/2023
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
In recent years, research on learning with noisy labels has focused on d...
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12/07/2022
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
Convolutional Neural Networks (CNNs) have demonstrated superiority in le...
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12/20/2020
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning
The predicament in semi-supervised few-shot learning (SSFSL) is to maxim...
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05/28/2020
TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples
The challenges of high intra-class variance yet low inter-class fluctuat...
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08/04/2019
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification
Deep neural networks have demonstrated advanced abilities on various vis...
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04/07/2019