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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Temporal action segmentation (TAS) is a critical step toward long-term videounderstanding. Recent studies follow a pattern that builds models based onfeatures instead of raw video picture information. However, we claim thosemodels are trained complicatedly and limit application scenarios. It is hardfor them to segment human actions of video in real time because they must workafter the full video features are extracted. As the real-time actionsegmentation task is different from TAS task, we define it as streaming videoreal-time temporal action segmentation (SVTAS) task. In this paper, we proposea real-time end-to-end multi-modality model for SVTAS task. More specifically,under the circumstances that we cannot get any future information, we segmentthe current human action of streaming video chunk in real time. Furthermore,the model we propose combines the last steaming video chunk feature extractedby language model with the current image feature extracted by image model toimprove the quantity of real-time temporal action segmentation. To the best ofour knowledge, it is the first multi-modality real-time temporal actionsegmentation model. Under the same evaluation criteria as full video temporalaction segmentation, our model segments human action in real time with lessthan 40% of state-of-the-art model computation and achieves 90% of the accuracyof the full video state-of-the-art model.

Streaming Video Temporal Action Segmentation In Real Time