DINO-Explorer uses ego-motion compensated semantic surprise from a frozen DINOv3 model and action-conditioned predictor to triage underwater events, retaining 78.8% of human-consensus events while cutting telemetry bandwidth by 48.2%.
Dino-wm: World models on pre-trained visual features enable zero-shot planning,
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DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Coding
DINO-Explorer uses ego-motion compensated semantic surprise from a frozen DINOv3 model and action-conditioned predictor to triage underwater events, retaining 78.8% of human-consensus events while cutting telemetry bandwidth by 48.2%.