Mask What Matters: Saliency-Guided Video Self-Supervised Learning for Autonomous Driving
Video self-supervised learning through masked spatiotemporal prediction has emerged as a promising paradigm for learning feature representations from unlabeled data. However, existing methods typically rely on random masking, which indiscriminately removes regions irrespective of their semantic or temporal relevance. In ego-centric driving videos, this can weaken the pretext signal since safety-critical cues such as pedestrians, vehicles, lane boundaries, and dynamic interactions often occupy only a small portion of the frame, yet are central to downstream perception. We introduce V-JEPA4A, a domain-specialized variant of V-JEPA for autonomous driving that is pre-trained on publicly avail...
| Authors | Christopher Lang, Alexander Braun, Abhinav Valada |
|---|---|
| Primary category | cs.CV |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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