You're currently viewing an old version of this dataset. To see the current version, click here.

Long-term Leap Attention, Short-term Periodic Shift for Video Classification

Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes T times longer sequence than the latter under the current attention of quadratic complexity. The existing works treat the temporal axis as a simple extension of spatial axes, focusing on shortening the spatio-temporal sequence by either generic pooling or local windowing without utilizing temporal redundancy.

Data and Resources

This dataset has no data

Cite this as

Hao Zhang, Lechao Cheng, Yanbin Hao, Chong-wah Ngo (2025). Dataset: Long-term Leap Attention, Short-term Periodic Shift for Video Classification. https://doi.org/10.57702/oy3jtirs

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field Value
Created January 3, 2025
Last update January 3, 2025
Defined In https://doi.org/10.1145/3503161.3547908
Author Hao Zhang
More Authors
Lechao Cheng
Yanbin Hao
Chong-wah Ngo
Homepage https://github.com/VideoNetworks/LAPS-transformer