在COCO 数据集上目标检测和语义分割的实验结果 Attention 可视化对比 论文信息 Shunted Self-Attention via Multi-Scale Token Aggregation https://arxiv.org/pdf/2111.15193.pdf
这种机制通过选择性地合并tokens来实现,以表示更大的对象特征,同时保留某些tokens以保留细粒度特征。这种聚合方式使得自注意力能够学习不同大小对象之间的关系,同时降低tokens数量和计算成本。 3. 探究Shunted Self-Attention如何通过Multi-Scale Token Aggregation实现 SSA通过以下方式实现Multi-Scale Token Aggregation: 分组...
此外,我们通过消融研究评估模型不同组件的效果 #博客煎饼果子不要果子:【多尺度 Attention】Shunted Self-Attention via Multi-Scale Token Aggregation
为了解决这个问题,作者提出了一个新颖且通用的策略—shunted self-attention(SSA)。SSA 的关键思想是将异构感受野大小注入标记:在计算自注意力矩阵之前,它选择性地合并标记以表示更大的对象特征,同时保持某些标记以保留细粒度特征。这种新颖的合并方案使 self-attention 能够学习不同大小的对象之间的关系,同时降低令牌数...
论文阅读笔记二十一:MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS(ICRL2016) 论文源址:https://arxiv.org/abs/1511.07122 tensorflow Github:https://github.com/ndrplz/dilation-tensorflow 摘要 该文提出了空洞卷积模型,在不降低分辨率的基础上聚合图像中不同尺寸的上下文信息,同时,空洞卷积扩大感受野的...
Guided-attention and gated-aggregation network for medical image segmentation 2024, Pattern Recognition Citation Excerpt : Multi-scale feature fusion is an effective way to improve the quality of the features to handle scale variations, and can be achieved via feature combination of high-level and lo...
Secondly, cascaded spatial pyramidal pooling (CSPP) were employed to fuse feature extraction branches with different receptive fields, enhancing the correlation between multi-scale features. Finally, a bottom-up attention fusion module was adopted to guide the cascading aggregation of high-level and low...
Shunted Self-Attention via Multi-Scale Token Aggregation Sucheng Ren1,2∗, Daquan Zhou1*, Shengfeng He2, Jiashi Feng3†, Xinchao Wang1† 1National University of Singapore, 2South China University of Technology, 3ByteDance Inc. oliverrensu@gmail.com, daquan.zhou@u.nus....
information captured from a large area while preserving spatial resolution, we adopt dilated convolutions to extract multi-scale features with rich context ... Q Yan,D Gong,JQ Shi,... - 《Pattern Recognition》 被引量: 0发表: 2022年 Attention-based Context Aggregation Network for Monocular Depth...
[2] R. Wang, Y. Shen, W. Zuo, S. Zhou, and N. Zheng, “TransVPR: Transformer-based placerecognitionwith multi-level attention aggregation.” [3] A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv Prepr. arXiv2010.11929, ...