[论文总结] Towards Robust Vision Transformer 说在前面 CVPR 2022,阿里安全,原文链接:arxiv.org/abs/2105.0792 官方开源代码:github.com/vtddggg/Robu 本文作于2022年05月04日 1. 解决的问题 Vision Transformer (ViT) 及其改进变体的最新进展表明,基于自注意力的网络在大多数视觉任务中都超过了传统的卷积神经网...
Vision Transformer必读系列之图像分类综述:Attention-based 将门创投 【AAAI2022】ShiftVIT: When Shift Operation Meets Vision Transformer 论文:【AAAI2022】When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism 代码:https://github.com/microsoft/SPACH B站作者讲解视频...
To mitigate this problem and facilitate the diagnosis of COVID-19, we developed a self-attention transformer-based approach having self-attention mechanism using CT slices. The architecture of transformer can exploit the ample unlabelled datasets using pre-training. The paper aims to compare the ...
To understand Vision Transformer, first we need to focus on the basics of transformer and attention mechanism. For this part I will follow the paperAttention is All You Need. This paper itself is an excellent read and the description/concepts below are mostly taken from there & understanding th...
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference Haoran You1,2,†,*, Yunyang Xiong2,∗, Xiaoliang Dai2, Bichen Wu2, Peizhao Zhang2, Haoqi Fan2, Peter Vajda2, Yingyan (Celi...
在为V7 选择图像之前,我使用不同的 CLIP 模型和 Visual Transformer 进行了广泛的测试。我发现 ViT 模型虽然表现出强大的性能,但缺乏与审美理解的一致性,因为它们没有接触到 CLIP 模型规模的审美样本,并且更需要数据。例如,一旦我将一些类似的排名添加到使用相似姿势的非常不同的图像上,他们就会将特定的视觉元素(如...
Wang, N., Zhou, W., Wang, J., & Li, H. (2021). Transformer meets tracker: exploiting temporal context for robust visual tracking. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition(pp. 1571–1580). Piscataway: IEEE. ...
关键字:Generalist Vision Transformer (GiT)、Universal Language Interface、Multi-task Learning、Zero-shot Transfer、Transformer 摘要 本文提出了一个简单而有效的框架,称为GiT,仅使用普通的ViT即可应用于各种视觉任务。受大型语言模型(LLMs)中广泛使用的多层Transformer架构(例如GPT)的普适性启发,我们寻求将其扩展应用...
Vision transformer adapter for dense predictions. In The Eleventh International Conference on Learning Representations (2023). Wang, X. et al. SCL-WC: cross-slide contrastive learning for weakly-supervised whole-slide image classification. Advances in Neural Information Processing Systems 35, 18009–...
其中,Swin Transformer(以CTrans Path为代表)和传统的卷积神经网络CNNs(如ResNet-50和REMEDIS模型)作为分层视觉主干,显示出在细胞核分割任务上的优势。尽管如此,基于Vision Transformer的模型(如UNI)虽然在结构上与前两者不同,但其在核分割方面的性能也表现出色。这表明即使是不同于传统层次化视觉主干的Transformers...