TL;DR
Inspired by human visual perception, where individuals first perceive an object globally and then focus on salient local details, the proposed hybrid CNN-Transformer architecture captures global representations and employs attention maps to refine task-relevant local features.
A novel adaptive fusion framework that adaptively combines CNN and Transformer features through learnable gating, attention-based feature integration, and explainable-AI methods is developed, intended to improve both computational efficiency and model interpretability.
Classification of fine-grained flower species is still a challenging task in computer vision because of high inter-class similarity, intra-class variability, illumination variations, and complex background conditions but Conventional CNN-based methods are good at capturing local spatial features but are generally inade...
Convolutional neural networks (CNNs) have long played a central role in computer vision due to their fast computation speed and high image feature extraction performance. CNNs can effectively learn diverse visual features ranging from low-level to high-level representations, resulting in high computational efficiency....
This article proposes ActNet, a novel deep convolutional neural network architecture that combines multi-scale feature learning with a focus-aware attention mechanism to address the problem of recognizing human actions from still images.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
