Abstract The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for coping with the complex and opaque nature of these architectures …

To address the problem of poor detection and under-utilization of the spatial relationship
between nodes in human pose estimation, a method based on an improved spatial temporal …

Recent breakthroughs in the Deep Learning field have resulted in neural models surpassing
human intelligence in a variety of specialized tasks. Modern neural networks have proven …

Online tracking is a feature of many state-of-the-art object trackers. When learning online,
the data is limited, so the tracker learns a sketch of the object's features. For a tracker to …

With many data breaches and spoofing attacks on our networks, it becomes imperative to
provide a reliable method for verifying the integrity of the source. Blockchain location-based …

Computer Vision (CV) is present everywhere around us; we live with this technology on a
daily basis. It can be found in our mobile phones, computers, cars, alarm systems, etc. It has …

This research explores practical applications of Transfer Learning and Spatial Attention
mechanisms using pre-trained models from an open-source simulator, CARLA (Car …

The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands
methods for coping with the complex and opaque nature of these architectures. The analysis …

Human-machine co-driving technology helps to reduce traffic accidents and improve driving
safety, and has become the frontier and hot research direction of intelligent driving. In the …

This study is devoted to the acquaintance, application, and implementation of computer
vision models in the automated Microsoft Custom Vision service, as well as comparing the …