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arXiv · 2501.05453

An Empirical Study of Autoregressive Pre-training from Videos

Jathushan Rajasegaran·Ilija Radosavovic·Rahul Ravishankar·Yossi Gandelsman·Christoph Feichtenhofer·Jitendra Malik
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Abstract

We empirically study autoregressive pre-training from videos. To perform our study, we construct a series of autoregressive video models, called Toto. We treat videos as sequences of visual tokens and train transformer models to autoregressively predict future tokens. Our models are pre-trained on a diverse dataset of videos and images comprising over 1 trillion visual tokens. We explore different architectural, training, and inference design choices. We evaluate the learned visual representations on a range of downstream tasks including image recognition, video classification, object tracking, and robotics. Our results demonstrate that, despite minimal inductive biases, autoregressive pre-training leads to competitive performance across all benchmarks. Finally, we find that scaling our video models results in similar scaling curves to those seen in language models, albeit with a different rate. More details at https://brjathu.github.io/toto/

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Jathushan Rajasegaran, Ilija Radosavovic, Rahul Ravishankar, Yossi Gandelsman, Christoph Feichtenhofer, Jitendra Malik. 2025-01-09. An Empirical Study of Autoregressive Pre-training from Videos. https://arxiv.org/abs/2501.05453

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