Abstract: Graph Neural Networks (GNNs) are rapidly becoming essential tools in deep learning, but their effectiveness when applied to images is often limited by challenges in graph representation.
Abstract: Fine-grained image classification (FGIC) remains a challenging task due to subtle inter-class differences and significant intra-class variations, particularly under limited training data.
Abstract: Medical image classification has been significantly improved by Convolutional Neural Networks (CNN), enabling efficient and accurate diagnosis, especially in detecting brain tumors. Despite ...
A CNN insider reportedly issues a “wake-up call” to Kaitlan Collins as her presence at an exclusive no-journalists party sparks backlash and questions about her image. Samuel Alito raises question ...
Abstract: Leaf blast disease is a significant constraint in world-wide rice production systems, necessitating effective monitoring for optimized crop-yield management. Satellite-derived land-surface ...
Abstract: Feature representation is crucial for hyperspectral image (HSI) classification. However, existing convolutional neural network (CNN)-based methods are limited by the convolution kernel and ...
Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...
The chattering class is consumed this morning by two interlocking questions: who will actually run CNN once the Paramount-Warner Bros. Discovery deal closes, and whether Bari Weiss is up to the job.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results