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

In this paper, we present a one-stage framework TriDet for temporal actiondetection. Existing methods often suffer from imprecise boundary predictionsdue to the ambiguous action boundaries in videos. To alleviate this problem, wepropose a novel Trident-head to model the action boundary via an estimatedrelative probability distribution around the boundary. In the feature pyramidof TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layerto mitigate the rank loss problem of self-attention that takes place in thevideo features and aggregate information across different temporalgranularities. Benefiting from the Trident-head and the SGP-based featurepyramid, TriDet achieves state-of-the-art performance on three challengingbenchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computationalcosts, compared to previous methods. For example, TriDet hits an average mAP of$69.3\%$ on THUMOS14, outperforming the previous best by $2.5\%$, but with only$74.6\%$ of its latency. The code is released tohttps://github.com/sssste/TriDet.

TriDet: Temporal Action Detection with Relative Boundary Modeling