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Neural networks : the official journal of the International Neural Network Society
Jan 24, 2025
Weakly-supervised fine-grained temporal action localization seeks to identify fine-grained action instances in untrimmed videos using only video-level labels. The primary challenge in this task arises from the subtle distinctions among various fine-g...
Neural networks : the official journal of the International Neural Network Society
Jan 24, 2025
To mitigate the shortage of labeled data, Few-Shot Classification (FSC) methods train deep neural networks (DNNs) on a base dataset with sufficient labeled data, and then adapt them to target tasks using a few labeled data. Despite notable progress, ...
Neural networks : the official journal of the International Neural Network Society
Jan 23, 2025
Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsity problems. However, caused by random augmentations (e.g., crop, mask, and reorder), existing metho...
Neural networks : the official journal of the International Neural Network Society
Jan 23, 2025
Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optimal graphs that reflect the underlying topology of the data. However, current MGRC approaches are limi...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
The current few-shot relational triple extraction (FS-RTE) techniques, which rely on prototype networks, have made significant progress. Nevertheless, the scarcity of data in the support set results in both intra-class and inter-class gaps in FS-RTE....
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Reconstruction-based methods achieve promising performance for visual anomaly detection (AD), relying on the underlying assumption that the anomalies cannot be accurately reconstructed. However, this assumption does not always hold, especially when s...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Unsupervised domain adaptation (UDA) aims to annotate unlabeled target domain samples using transferable knowledge learned from the labeled source domain. Optimal transport (OT) is a widely adopted probability metric in transfer learning for quantify...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Sequential recommendation models aim to predict the next item based on the sequence of items users interact with, ordered chronologically. However, these models face the challenge of data sparsity. Recent studies have explored cross-domain sequential...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of the underlying data distributions over time (concept dr...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Shadow removal remains a challenging visual task aimed at restoring the original brightness of shadow regions in images. Many existing methods overlook the implicit clues within non-shadow regions, leading to inconsistencies in the color, texture, an...