Effect of Factorized Latent Dynamics Auxiliary Objectives on Video-JEPA Transfer Learning for Downstream Video Reasoning

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 goal: What is the effect of factorized latent dynamics auxiliary objectives on the transfer learning performance of Video-JEPA when evaluated on downstream video reasoning tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.