Video Self-Stitching Graph Network for Temporal Action Localization

Chen Zhao, Ali Kassem Thabet, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations


Temporal action localization (TAL) in videos is a challenging task, especially due to the large variation in action temporal scales. Short actions usually occupy a major proportion in the datasets, but tend to have the lowest performance. In this paper, we confront the challenge of short actions and propose a multi-level cross-scale solution dubbed as video self-stitching graph network (VSGN). We have two key components in VSGN: video self-stitching (VSS) and cross-scale graph pyramid network (xGPN). In VSS, we focus on a short period of a video and magnify it along the temporal dimension to obtain a larger scale. We stitch the original clip and its magnified counterpart in one input sequence to take advantage of the complementary properties of both scales. The xGPN component further exploits the cross-scale correlations by a pyramid of cross-scale graph networks, each containing a hybrid module to aggregate features from across scales as well as within the same scale. Our VSGN not only enhances the feature representations, but also generates more positive anchors for short actions and more short training samples. Experiments demonstrate that VSGN obviously improves the localization performance of short actions as well as achieving the state-of-the-art overall performance on THUMOS-14 and ActivityNet-v1.3. VSGN code is available at
Original languageEnglish (US)
Title of host publication2021 IEEE/CVF International Conference on Computer Vision (ICCV)
ISBN (Print)978-1-6654-2813-2
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2022-03-09
Acknowledgements: This work was supported by the King
Abdullah University of Science and Technology (KAUST)
Office of Sponsored Research through the Visual Computing Center (VCC) funding.


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