Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning
Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning. Reconstruction-based approaches such as MAE and VideoMAE learn representations by recovering masked visual content \cite{he2022mae,tong2022videomae}, while contrastive methods such as CLIP learn semantically meaningful embedding spaces through representation alignment \cite{radford2021clip}. In this work, we introduce a Momentum-Guided Semantic Forecasting framework (MoFore) for self-supervised video representation learning. Instead of optimizing for pixel-level reconstruction or task-specific semantic alignment, the proposed meth...
| Authors | Qinwu Xu |
|---|---|
| Primary category | cs.CV |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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