Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning, or few-shot learning, aims to effectively train a model using only a small amount of …

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range
of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

Recent progress in large-scale pre-training has led to the development of advanced vision-
language models (VLMs) with remarkable proficiency in comprehending and generating …

This paper investigates the application of neural basis expansion analysis with exogenous
variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps …

Graph neural networks are widely used tools for graph prediction tasks. Motivated by their
empirical performance, prior works have developed generalization bounds for graph neural …

Video summarization aims to distill the most important information from a source video into
either an abridged video clip or a textual narrative. Existing methods often treat the …

Existing fine-tuning methods for computer vision tasks primarily focus on re-weighting the
knowledge learned from the source domain during pre-training. They aim to retain beneficial …

The advent of large-scale pretrained language models (PLMs) has contributed greatly to the
progress in natural language processing (NLP). Despite its recent success and wide …

Video summarization aims to create short, accurate, and cohesive summaries of longer
videos. Despite the existence of various video summarization datasets, a notable limitation …