Object detection is one of the predominant and challenging problems in computer vision.
Over the decade, with the expeditious evolution of deep learning, researchers have …

In the realm of computer vision, Deep Convolutional Neural Networks (DCNNs) have
demonstrated excellent performance. Video Processing, Object Detection, Image …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Transformer-based methods have shown impressive performance in low-level vision tasks,
such as image super-resolution. However, we find that these networks can only utilize a …

Weeds are among the major threats to cotton production. Overreliance on herbicides for
weed control has accelerated the evolution of herbicide-resistance in weeds and caused …

Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

Pre-trained vision-language models (VLMs) learn to align vision and language
representations on large-scale datasets, where each image-text pair usually contains a bag …

This paper implements a systematic methodological approach to review the evolution of
YOLO variants. Each variant is dissected by examining its internal architectural composition …

Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …

Deep learning has achieved remarkable results in many computer vision tasks. Deep neural
networks typically rely on large amounts of training data to avoid overfitting. However …