ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning

Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan Yuille, Cihang Xie

Keywords: Autoregressive Pretraining; Self-Supervised Video Representation Learning

Abstract:

This paper presents a new self-supervised video representation learning framework \textbf{ARVideo}, which \textit{autoregressively} predict the next video token in a tailored sequence order. Two key designs are included. First, we organize autoregressive video tokens into clusters that span both \textit{spatially} and \textit{temporally}, thereby enabling a richer aggregation of contextual information compared to the standard spatial-only or temporal-only clusters. Second, we adopt a randomized spatiotemporal prediction order to facilitate learning from multi-dimensional data, addressing the limitations of a handcrafted spatial-first or temporal-first sequence order. Extensive experiments establish ARVideo as an effective paradigm for self-supervised video representation learning. For example, when trained with the ViT-B backbone, ARVideo competitively attains 81.2% on Kinetics-400 and 70.9% on Something-Something V2, which are on par with the strong benchmark set by VideoMAE. Importantly, ARVideo also demonstrates higher training efficiency, \ie, it trains 14% faster and requires 58% less GPU memory compared to VideoMAE.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 7776