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Argument Clarification about 2d-tan HOT 3 CLOSED

microsoft avatar microsoft commented on May 7, 2024
Argument Clarification

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Comments (3)

Sy-Zhang avatar Sy-Zhang commented on May 7, 2024

It's the output downsample rate in terms of input. For example, if there are NUM_SAMPLE_CLIPS input clips, then the height and width of the output score map should be NUM_SAMPLE_CLIPS//TARGET_STRIDE.

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AmeenAli avatar AmeenAli commented on May 7, 2024

It's the output downsample rate in terms of input. For example, if there are NUM_SAMPLE_CLIPS input clips, then the height and width of the output score map should be NUM_SAMPLE_CLIPS//TARGET_STRIDE.
@Sy-Zhang
Thanks,
can you tell for how many epochs training was done? because I am still not able to reproduce the same results mentioned in the paper, Thanks

from 2d-tan.

Sy-Zhang avatar Sy-Zhang commented on May 7, 2024

It's the output downsample rate in terms of input. For example, if there are NUM_SAMPLE_CLIPS input clips, then the height and width of the output score map should be NUM_SAMPLE_CLIPS//TARGET_STRIDE.
@Sy-Zhang
Thanks,
can you tell for how many epochs training was done? because I am still not able to reproduce the same results mentioned in the paper, Thanks

The epoch for training is defined as MAX_EPOCH, which is set to 100 for all datasets.

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