Reproduce “Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Santosh Premi
Researchers at the University of Würzburg empirically investigated how various auxiliary objectives influence Video-JEPA's ability to learn balanced video representations, proposing a Factorized World-Model with Hard-Region-Weighted Latent Dynamics (FWM-HW-LD) that yielded strong performance across appearance, temporal, and motion understanding tasks. The study provides empirical evidence of capacity trade-offs among different objectives under small-scale training conditions.
Key insights
- Many auxiliary objectives, when applied to a shared latent space, introduce capacity trade-offs, improving one downstream capability while degrading another, especially within a fixed small-scale budget.
- Explicit structural factorization of the latent space into dedicated appearance and dynamics subspaces, combined with targeted dynamics prediction, can mitigate these trade-offs and lead to more balanced video representations.
- Pixel-prediction objectives (e.g., predicting RGB deltas) for V-JEPA are generally ineffective for learning high-level semantic representations, supporting the core JEPA principle of prediction in abstract latent space.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.