English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Video understanding usually requires expensive computation that prohibits itsdeployment, yet videos contain significant spatiotemporal redundancy that canbe exploited. In particular, operating directly on the motion vectors andresiduals in the compressed video domain can significantly accelerate thecompute, by not using the raw videos which demand colossal storage capacity.Existing methods approach this task as a multiple modalities problem. In thispaper we are approaching the task in a completely different way; we are lookingat the data from the compressed stream as a one unit clip and propose that theresidual frames can replace the original RGB frames from the raw domain.Furthermore, we are using teacher-student method to aid the network in thecompressed domain to mimic the teacher network in the raw domain. We showexperiments on three leading datasets (HMDB51, UCF1, and Kinetics) thatapproach state-of-the-art accuracy on raw video data by using compressed data.Our model MFCD-Net outperforms prior methods in the compressed domain and moreimportantly, our model has 11X fewer parameters and 3X fewer Flops,dramatically improving the efficiency of video recognition inference. Thisapproach enables applying neural networks exclusively in the compressed domainwithout compromising accuracy while accelerating performance.

Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain