Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Despite substantial progress in the field of deep learning, overfitting persists as a critical
challenge, and data augmentation has emerged as a particularly promising approach due to …

Over the past decade, convolutional neural networks (CNN) have shown very competitive
performance in medical image analysis tasks, such as disease classification, tumor …

With an increase in both global warming and the human population, forest fires have
become a major global concern. This can lead to climatic shifts and the greenhouse effect …

In recent years, deep learning has been successfully applied to hyperspectral image
classification (HSI) problems, with several convolutional neural network (CNN) based …

In this paper, we address the problem of forest fires' early detection and segmentation in
order to predict their spread and help with fire fighting. Techniques based on Convolutional …

For emergency rescue and damage assessment after an earthquake, quick detection of
seismic landslides in the affected areas is crucial. The purpose of this study is to quickly …

Video grounding aims to localize the temporal segment corresponding to a sentence query
from an untrimmed video. Almost all existing video grounding methods fall into two …

From the perspective of historical review, the methodology of economics develops from
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …