八度捲積提出將特徵圖中不同解析度的特徵分開進行捲積運算，不但可以有效的減少空間和計算上的冗餘，還可以提高捲積網路的精度。在這篇論文中，我們提出了一個更加快速的八度捲積--FOCAM進一步的減少八度捲積在捲積網路中的計算成本。FOCAM和八度捲積類似，都是一個簡單,通用且即插即用捲積運算，並且FOCAM和八度捲積都把輸入和輸出的特徵圖都切成不同解析度來進行運算和存取，但FOCAM使用注意力機制來取代八度捲積中不同解析特徵圖資訊交換的部份，始用多尺度來取代高解析特徵圖資訊更新的部份並且用捲積分解來進一步的減少計算成本。在資料集為ImageNet的實驗中，我們可以發現FOCAM可以比一般的機網路減少42\%到54\%的計算成本，且可以比八度捲積減少24\% 到 38\%的計算成本在相近的top-1 和top-5的精度上面。

Octave convolution that separates the operations for different resolutions is an effective method to reduce the spatial redundancy and improve the accuracy in Convolution Neural Networks (CNN). In this paper, we propose a faster version of octave convolution, FOCAM, which can further reduce the computational cost of CNNs. Similar to the octave convolution, FOCAM creates a single, generic, plug-and-play convolution unit and divides the input and output feature maps into the domains of different resolutions, but without explicit information exchange among them. Instead, FOCAM employs the attention mechanism to collect information conveyed at different resolutions. In addition, multi-scaled convolution kernels are utilized to learn different sized spatial features, and using factorizing convolutions can effectively reduce the computation cost.Experiments on various depth ResNet with ImageNet data-set have shown that FOCAM can reduce 42\% to 54\% operations of the original models, and save 24\% to 38\% FLOPS of the models using octave convolutions, with similar top-1 and top-5 accuracy.

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