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

Temporally localizing actions in a video is a fundamental challenge in videounderstanding. Most existing approaches have often drawn inspiration from imageobject detection and extended the advances, e.g., SSD and Faster R-CNN, toproduce temporal locations of an action in a 1D sequence. Nevertheless, theresults can suffer from robustness problem due to the design of predeterminedtemporal scales, which overlooks the temporal structure of an action and limitsthe utility on detecting actions with complex variations. In this paper, wepropose to address the problem by introducing Gaussian kernels to dynamicallyoptimize temporal scale of each action proposal. Specifically, we presentGaussian Temporal Awareness Networks (GTAN) --- a new architecture that novellyintegrates the exploitation of temporal structure into an one-stage actionlocalization framework. Technically, GTAN models the temporal structure throughlearning a set of Gaussian kernels, each for a cell in the feature maps. EachGaussian kernel corresponds to a particular interval of an action proposal anda mixture of Gaussian kernels could further characterize action proposals withvarious length. Moreover, the values in each Gaussian curve reflect thecontextual contributions to the localization of an action proposal. Extensiveexperiments are conducted on both THUMOS14 and ActivityNet v1.3 datasets, andsuperior results are reported when comparing to state-of-the-art approaches.More remarkably, GTAN achieves 1.9% and 1.1% improvements in mAP on testing setof the two datasets.

Gaussian Temporal Awareness Networks for Action Localization