Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with
merely a handful of annotated instances. Meta-learning has been widely adopted for such a …

Nowadays, scene text recognition has attracted more and more attention due to its various
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …

Existing video self-supervised learning methods mainly rely on trimmed videos for model
training. They apply their methods and verify the effectiveness on trimmed video datasets …

Few-shot relation classification (FSRC) focuses on recognizing novel relations by learning
with merely a handful of annotated instances. Meta-learning has been widely adopted for …

Abstract Cross-Domain Few-Shot Learning (CD-FSL) to recognize new categories in a new
domain with few samples has attracted significant attention. Recently, task-specific CD-FSL …

Domain adaptation is an effective solution to data scarcity in low-resource scenarios.
However, when applied to token-level tasks such as bioNER, domain adaptation methods …

Abstract Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success in
training models that can quickly adapt to new tasks via one or few stochastic gradient …

Meta-learning has recently promoted few-shot text classification, which identifies target
classes based on information transferred from source classes through a series of small tasks …

The predominant method for computing confidence intervals (CI) in few-shot learning (FSL)
is based on sampling the tasks with replacement, ie\allowing the same samples to appear in …

Meta-learning (ML) has emerged as a promising direction in learning models under
constrained resource settings like few-shot learning. The popular approaches for ML either …