You will be given a definition of a task first, then some input of the task.
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

An octave convolutional layer BIBREF0 factorizes the output feature maps of a convolutional layer into two groups. The resolution of the low-frequency feature maps is reduced by an octave height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves dividing by $2^t$, where $t=1,2,3$ and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer, and an example with three groups and reductions of one and two octaves is depicted in Fig. FIGREF1.
Output:
How is octave convolution concept extended to multiple resolutions and octaves?