Summary
The Zero-shot World Model (ZWM) significantly narrows the data gap between AI and human children for visual competence. Its variant, BabyZWM, trained on a single child's visual experience, matches state-of-the-art models on diverse visual-cognitive tasks without task-specific training. This work presents a crucial blueprint for developing efficient and flexible AI learning from human-scale data.
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