导读:论文提出CoAE少样本目标检测算法,该算法使用non-local block来提取目标图片与查询图片间的对应特征,使得RPN网络能够准确的获取对应类别对象的位置,另外使用类似SE block的squeeze and co-excitation模块来根据查询图片加强对应的特征纬度,最后结合margin based ranking loss达到了state-of-the-art,论文创新点满满。 论...
论文提出CoAE少样本目标检测算法,该算法使用non-local block来提取目标图片与查询图片间的对应特征,使得RPN网络能够准确的获取对应类别对象的位置,另外使用类似SE block的squeeze and co-excitation模块来根据查询图片加强对应的特征纬度,最后结合margin based ranking loss达到了state-of-the-art,论文创新点满满,值得一读...
NeurIPS 2019 | 基于Co-Attention和Co-Excitation的少样本目标检测 论文提出CoAE少样本目标检测算法,该算法使用non-local block来提取目标图片与查询图片间的对应特征,使得RPN网络能够准确的获取对应类别对象的位置,另外使用类似SE block的squeeze and co-excitation模块来根据查询图片加强对应的特征纬度,最后结合margin based...
论文提出CoAE少样本目标检测算法,该算法使用non-local block来提取目标图片与查询图片间的对应特征,使得RPN网络能够准确的获取对应类别对象的位置,另外使用类似SE block的squeeze and co-excitation模块来根据查询图片加强对应的特征纬度,最后结合margin based ranking loss达到了state-of-the-art,论文创新点满满,值得一读 ...
We embed the proposed co-attention block into a U-shaped Siamese network for fulfilling the image co-segmentation. It is proven to be able to improve the performance effectively in the experiments. To our best knowledge, it leads to the currently best results on Internet dataset and iCoseg ...
这个模块可以被插入在整个pipeline的feature extract阶段的各个block之间。 Experiment 插在后面会有一个明显的提升。 对各种time aggregation方法都有一个提升。 总体上能提一个点的样子。 Discussion 这篇文章看完之后觉得就是non-local [1] 用在了video-reid上,相似的工作我看到的还有两篇 [2, 3]。不过这篇文...
DenseNet Convolution block attention module Depthwise separable convolution 1. Introduction Coronavirus disease 2019 (COVID-19), named SARS-CoV-2 by the International Committee on Taxonomy of Viruses (ICTV), is a highly infectious respiratory disease. More than 170 million confirmed COVID-19 cases an...
(GPA). Furthermore, we effectively combine GPA with Transformer in encoder part of model. It can not only highlight the foreground information of samples, but also reduce the negative influence of background information on the segmentation results. Meanwhile, we introduced the sMLP block to ...
Y1 is only processed by the attention block (AB), thereby it is the nearest output to the segmentation prediction of the baseline. While Sp is the segmentation results by fusing multiple level feature maps, which would achieve the goal of both high resolution and rich semantics by combining ...
CBAM: Convolutional block attention module Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 3-19 CrossrefGoogle Scholar [18] Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, Q. Hu ECA-Net: Efficient channel attention for deep convolutional neural networks Proceedings...