To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

Abstract Development of transformer-based text-to-image models is impeded by its slow
generation and complexity, for high-resolution images. In this work, we put forward a …

Plants play a crucial role in supplying food globally. Various environmental factors lead to
plant diseases which results in significant production losses. However, manual detection of …

Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Object Detection is the task of classification and localization of objects in an image or video.
It has gained prominence in recent years due to its widespread applications. This article …

Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …

The advent of artificial intelligence (AI) has significantly impacted the traditional judicial
industry. Moreover, recently, with the development of the concept of AI-generated content …

Self-attention has the promise of improving computer vision systems due to parameter-
independent scaling of receptive fields and content-dependent interactions, in contrast to …