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LeAVJEPA: A Minimalist Architecture for Audio-Visual Self-Supervised Learning
Prior audio-visual self-supervised learning methods rely on mechanisms such as EMA target encoders, prediction heads, reconstruction decoders, and contrastive losses. We introduce LeAVJEPA, the first audio-visual encoder trained under LeJEPA's collapse-free objective. A single early-fusion Vision Transformer processes...
Temporal Self-Distillation: Learning Visual State Tracking in Videos Without Supervision
This work introduces S$^3$T (Self-Supervised Self-Distillation over Time), which is the first fully self-contained framework for continuous video state tracking and treats temporal sampling density as privileged information, based on the hypothesis that a denser view of the same clip recovers the running state more acc...