What does "Temporal extent" mean? I am reading Long-term Temporal Convolutions for Action Recognition and under the Section 3.1, I read this:

To investigate the impact of long-term temporal convolutions, we here study network inputs with different temporal extents.....

and then

As illustrated in Figure 2, the temporal resolution in our 60f network corresponds to 60, 30, 15, 7 and 3 frames for each of the five convolutional layers. In comparison, the temporal resolution of the 16f network is reduced more drastically to 16, 8, 4, 2 and 1 frame at each convolutional layer. We believe that preserving the temporal resolution at higher convolutional layers should enable learning more complex temporal patterns. The space-time resolution for the outputs of the fifth convolutional layers is $3 \times 3 \times 1$ and $1 \times 1 \times 3$ for the 16f and 60f networks respectively. The two networks have a similar number of parameters in the fc6 layer and the same number of parameters in all other layers. For a systematic study of networks with different input resolutions we also evaluate the effect of increased temporal resolution $t \in \{20, 40, 60, 80, 100\}$ and varying spatial resolution of $\{58 \times 58, 71 \times 71\}$ pixels.

How can we reduce temporal resolution of a ConvNet at each convolutional layer.
What does preserving the temporal resolutional mean and what does complex temporal resolution mean?
I've taken courses on Convolutional Neural Network and never heard of anything such as &quot;temporal extent&quot; or &quot;temporal resolutions&quot;. Searching about them gives links to other research paper, which still use the term without describing its meaning.
Anyone, kindly put some light on it.
Temporal extent is the same as duration. It is measured in units of time, like second. For example, 1 second is a duration or temporal extent.