To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/
网络最前面是分辨率最低的子网络(coarest level network),在这个子网络最后,是“upconvolution layer”,将重建的低分辨率图像放大为高分辨率图像,然后和高一层的子网络的输入连接在一起,作为上层网络的输入。 再看单个 CNN 的结构:在第一层卷积层后,叠加了19个 ResBlock,最后一个卷积层将feature map 转化为输出...
如图2所示,MGCU由两部分组成,分别是单尺度图卷积块(single-scale graph convolution blocks, SS-GCB) 和跨尺度融合块(cross-scale fusion blocks, CS-FB)。SS-GCB分为两个步骤,分别是 (1)图卷积,三个尺度(第2个和第3个尺度数据由原数据对应关节点相加平均求得)的数据各自经过图卷积,其中图卷积的矩阵As是可...
To interact with local and global features, we combine this module with graph convolution to design a multi-scale dynamic graph convolutional network (MDGNet). In summary, the contributions made in this paper are as follows: (1) Based on the characteristics of DR image lesion features, we...
multi-scale information. Finally, convolution with a kernel size of 1 × 1 is performed to reduce the dimensionality of\(Z_{1} ,Z_{2} ,Z_{3}\)in the channel dimension, and there output is respectively input into the residual module of the next stage to extract multi-scale ...
By constructing the convolution wavefield objective function, the source difference between simulated and observed data is ignored, thus avoiding the source wavelet estimation. Theoretically, this process has no restriction on the accuracy of the wavelet, and a multi-scale inversion strategy can be ...
nn.functional as F class DynamicMultiHeadConv(nn.Module): global_progress = 0.0 def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, heads=4, squeeze_rate=16, gate_factor=0.25): super(DynamicMultiHeadConv, self).__init__() self.norm = nn....
轻量级网络设计通过设计一些轻量级操作(如depth-wise convolution)来构建新的网络。 输入分辨率是影响CNN计算量和性能的重要因素。对于同一网络,更高的分辨率通常会导致更大的FLOPs和更高的精度。虽然分辨率较小的模型性能较低,但FLOPs也较小,...
Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imagingarxiv-2022KalantariMulti-scale, Flow High Dynamic Range Imaging via Gradient-aware Context Aggregation NetworkPR-2022KalantariGradient information Labeled from Unlabeled: Exploiting Unlabeled Data for Few-shot Deep HDR DeghostingCVPR...
et al. Cam-convs: Camera-aware multi-scale convolutions for single-view depth. In Proc. IEEE Conf. Comput. Vis. Pattern Recog., 11818–11827, https://doi.org/10.1109/CVPR.2019.01210 (2019). Yin, W. et al. Learning to recover 3d scene shape from a single image. In Proc. IEEE ...