采用4种融合范式：“best modality”, “concatenation” (early), “uniform combination” (late) and...
使用VOC2007和VOC 2012训练集进行ForthePascalVOC2007test集训练后,输入尺寸为300×300的网络在速度为35.0FPS(帧秒)时达到78.5%mAP(平均平均精度),而使用Nvidia Titan X GPU的网络以512×512比例输入达到80.8%mAPat16.6FPS。所提出的网络显示了最先进的mAP,比传统的SSD,YOLO,Faster-RCNN和RFCN更好。而且,它比Fast...
The reconstruction generator can effectively generate a sophisticated probability map, which is used to calculate optimal distortion measurement and further provides a better guidance for adaptive information embedding. Comprehensive experimental results show that, with the same discriminant network, the anti-...
全局特征的通道注意力计算公式g(X),与L(X)的不同点就是,对输入的X先进行一次全局平均池化操作Global Average Pooling (GAP)。 计算之后的权重值用来对输入特征X做注意力操作后得到输出X'公式如下,而局部通道信息与输入特征保持有相同的尺寸,因此两者相加需要采取广播操作,⊗ 表示两个feature map对应元素相乘。 X...
For instance, in [1], higher-level DCNN layers are fused into a single feature map via concatenation. In [18], multiple feature-layers based on the DCNN or the hourglass network are concatenated as well to exploit the contextual information at different scales. Fig. 4. Multi-scale context ...
Concatenation layer The concatenation layer includes two reshape operations, a concatenation operation and a convolution. Supposing the size of capsule from convolutional capsule layer is [b, c, v, h, w] and the size of external feature map is [b, n, h, w]. The procedure of the concatenat...
In FSSD19, a feature fusion module is designed to generate a novel feature map and constructed the feature pyramid. In the feature fusion module, multi-scale feature maps are combined through concatenation operators. Based on FSSD, the FS-SSD with an extra scaling branch and the spatial ...
Figure 1. single “hourglass” 模块例示. 每一个 box 对应一个 residual 模块. Residual Unit: 采用residual unit 来构建 hourglass 网络 block. 但其只能捕捉一个尺度的视觉特征和语义. Stacked Hourglass Network 训练的中间监督处理: Figure 1.1 中间监督处理. 网络输出 heatmaps(蓝色框) ,其后添加训练 loss...
The feature map concatenation stream provides additional semantic information to the model without degrading the detection speed. By combining the output of these two streams, the enhanced feature map, which improves the detection of a small object, is generated. Experimental results show that the ...
This allows the concatenation of feature maps with different spatial resolutions. The GPCA is then performed on each sample set, consisting of one or more feature maps. These sets are obtained by concatenating the reshaped feature map or the reshaped combined feature map concatenated along the ...