To reduce the spatial dimensional inaccuracy due to upsampling in the traditional CNN framework, we develop a novel grasping visual architecture referred to as High resolution grasp nerual network ...

To reduce the spatial dimensional inaccuracy due to upsampling in the traditional CNN framework, we develop a novel grasping visual architecture referred to as High resolution grasp nerual network ...

Abstract: Convolutional Neural Networks (CNNs) and Transformer are two powerful representation learning techniques for visual tracking. Although CNNs can effectively reduce local redundancy via ...

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...

Reading a book, distinguishing faces or navigating traffic while driving is something human beings can easily do. Today, we have state of the art deep learning models like: Transformers, Convolutional ...