Machine learningDeep learning / NLP / CV
弱监督视觉变换器
弱监督视觉变换器 (WS-ViT) 在缺乏精确像素级标注的图像数据上训练视觉变换器，转而使用成本更低、噪声更大的监督信号，例如图像级类别标签、边界框或网络抓取文本。变换器的全局自注意力机制使其特别擅长从这些不完整的标签中定位物体和学习判别性特征。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR). link ↗
- Zhou, Z.-H. (2022). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. DOI: 10.1093/nsr/nwx106 ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Vision Transformer (WS-ViT). ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-vision-transformer
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →