Re2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization

Chen Zhao, Shuming Liu, Karttikeya Mangalam, Bernard Ghanem

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

8 Scopus citations


Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challenge. Most methods can only train on pre-extracted features without optimizing them for the localization problem, consequently limiting localization performance. In this work, to extend the potential in TAL networks, we propose a novel end-to-end method Re 2 TAL, which rewires pretrained video backbones for reversible TAL. Re 2 TAL builds a backbone with reversible modules, where the input can be recovered from the output such that the bulky intermediate activations can be cleared from memory during training. Instead of designing one single type of reversible module, we propose a network rewiring mechanism, to transform any module with a residual connection to a reversible module without changing any parameters. This provides two benefits: (1) a large variety of reversible networks are easily obtained from existing and even future model designs, and (2) the reversible models require much less training effort as they reuse the pre-trained parameters of their original non-reversible versions. Re 2 TAL, only using the RGB modality, reaches 37.01% average mAP on ActivityNet-v1.3, a new state-of-the-art record, and mAP 64.9% at tIoU=0.5 on THUMOS-14, outperforming all other RGB-only methods.
Original languageEnglish (US)
Title of host publication2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
StatePublished - Aug 22 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-08-29
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 and SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence.


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