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 ...

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 ...

本研究针对铁路运输安全中紧固件缺陷检测的难题，采用非破坏性评估(NDE)技术，对比分析了Vision Transformer(ViT)、Data-efficient ...

Vision transformers (ViTs) are powerful artificial intelligence (AI) technologies that can identify or categorize objects in images -- however, there are significant challenges related to both ...

First, similar to how the Transformer works, the Vision Transformer is supervised, meaning the model is trained on a dataset of images and their corresponding labels. Convert the patch into a vector ...

为解决多模态MRI脑肿瘤分割中模态间互补信息提取与CNN-Transformer特征融合的难题，研究人员提出ViTR-Net架构。该模型通过并行 ...

Breast cancer remains a significant public health challenge, prompting extensive research to develop accurate and efficient diagnostic methods. In recent years, the application of artificial ...

At the Google Cloud Next conference, Google introduced a new computer vision platform, Vertex AI Vision, that simplifies the process of building analytics based on live camera streams and videos.