Scaling of Factorized Latent Dynamics Representation Accuracy in Video-JEPA with Multimodal Pretraining

Abstract

Abstract: Joint-Embedding Predictive Architectures (JEPA) are a promising framework for self-supervised video representation learning, yet the behavior of auxiliary objectives in small-scale Video-JEPA training is not well characterized. We report a small-scale empirical study of 18 auxiliary objective variants for Video-JEPA across two pretraining regimes: single-dataset (UCF-101) and mixed-dataset (UCF-101 + Something-Something V2 + ImageNet-100). We evaluate frozen representations on three complementary benchmarks: Diving-48 (fine-grained motion), SomethingSomething V2 (temporal reasoning), and Image

Research Question

How does the representation accuracy of factorized latent dynamics in Video-JEPA scale when pretrained on large-scale multimodal video-text corpora compared to single-domain video datasets?

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