Transformer 架构因其强大的通用性而备受瞩目，它能够处理文本、图像或任何类型的数据及其组合。其核心的“Attention”机制通过计算序列中每个 token 之间的自相似性，从而实现对各种类型数据的总结和生成。在 Vision Transformer 中，图像首先被分解为正方形图像块 ...

多模态面部表情识别研究综述2021-2025年，系统分析Vision Transformer（ViT）与可解释AI（XAI）方法在融合策略、数据集及性能提升中的应用，指出ViT通过长距离依赖建模提升分类准确率，但存在隐私风险、数据不平衡及高计算成本等挑战，未来需结合隐私保护技术与 ...

The self-attention-based transformer model was first introduced by Vaswani et al. in their paper Attention Is All You Need in 2017 and has been widely used in natural language processing. A ...

In the last decade, convolutional neural networks (CNNs) have been the go-to architecture in computer vision, owing to their powerful capability in learning representations from images/videos.

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications ...