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

State-of-the-art methods for video action recognition commonly use anensemble of two networks: the spatial stream, which takes RGB frames as input,and the temporal stream, which takes optical flow as input. In recent work,both of these streams consist of 3D Convolutional Neural Networks, which applyspatiotemporal filters to the video clip before performing classification.Conceptually, the temporal filters should allow the spatial stream to learnmotion representations, making the temporal stream redundant. However, we stillsee significant benefits in action recognition performance by including anentirely separate temporal stream, indicating that the spatial stream is"missing" some of the signal captured by the temporal stream. In this work, wefirst investigate whether motion representations are indeed missing in thespatial stream of 3D CNNs. Second, we demonstrate that these motionrepresentations can be improved by distillation, by tuning the spatial streamto predict the outputs of the temporal stream, effectively combining bothmodels into a single stream. Finally, we show that our Distilled 3D Network(D3D) achieves performance on par with two-stream approaches, using only asingle model and with no need to compute optical flow.

D3D: Distilled 3D Networks for Video Action Recognition