This paper presents MOAT, a family of neural networks that build on top of MObile
convolution (ie, inverted residual blocks) and ATtention. Unlike the current works that stack …

In recent years, transformer-based models have dominated panoptic segmentation, thanks
to their strong modeling capabilities and their unified representation for both semantic and …

Smart robots in factories highly depend on Computer Vision (CV) tasks, eg object detection
and recognition, to perceive their surroundings and react accordingly. These CV tasks can …

Rooftop detection has numerous applications such as change detection in human
settlements, land encroachments, planning routes to rural areas and estimation of solar …

Purpose: This study was done to explore the utility of artificial intelligence (AI) and machine
learning in the diagnosis and grouping of intraocular retinoblastoma (iRB). Methods: It was a …

Abstract We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of
word-level text spotting and geometric layout analysis. HTS can recognize text in an image …

Training deep neural networks (DNNs) takes significant time and resources. A practice for
expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model …

Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-
of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper …

Purpose The purpose of this study was to evaluate an artificial intelligence (AI) solution for
estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two …

The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-
tech devices such as robots and sensors that are interconnected to assess or monitor …