The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...

The autonomous station's integration of hardware and AI architecture offers a significant departure from static CCTV setups ...

The field of computer vision has witnessed significant advancements in recent years, driven by the development of deep learning models and the availability of large-scale datasets. However, despite ...

Recently, inspired by deep learning, SU models based on neural networks have been proposed to more effectively extract and handle nonlinear features. Nevertheless, the convolution strategies employed ...

What is a “Convolutional Neural Network” (CNN ... Optical character recognition Medical imaging diagnostics Object detection and tracking The distinguishing feature of CNNs lies in their ...

AlexNet revolutionized the use of neural networks for computer vision, creating one of the underpinnings of generative AI.

In this paper, we take a different perspective on feature aggregation, and propose a dynamic graph contrastive network (DGC-Net) for video object detection, including three improvements against ...

The method combines a convolutional neural network with bidirectional long short-term memory, attention mechanism, and natural gradient boosting. A research group led by scientists from the Hong ...