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

When recognizing a long-range activity, exploring the entire video isexhaustive and computationally expensive, as it can span up to a few minutes.Thus, it is of great importance to sample only the salient parts of the video.We propose TimeGate, along with a novel conditional gating module, for samplingthe most representative segments from the long-range activity. TimeGate has twonovelties that address the shortcomings of previous sampling methods, asSCSampler. First, it enables a differentiable sampling of segments. Thus,TimeGate can be fitted with modern CNNs and trained end-to-end as a single andunified model.Second, the sampling is conditioned on both the segments andtheir context. Consequently, TimeGate is better suited for long-rangeactivities, where the importance of a segment heavily depends on the videocontext.TimeGate reduces the computation of existing CNNs on three benchmarksfor long-range activities: Charades, Breakfast and MultiThumos. In particular,TimeGate reduces the computation of I3D by 50% while maintaining theclassification accuracy.

TimeGate: Conditional Gating of Segments in Long-range Activities