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G2D boosts zero-shot image classification accuracy by up to 27.42 percentage points by effectively combining generative verification with discriminative retrieval.
Multi-domain graph pre-training suffers from significant data redundancy; MDGMIX leverages boundary-aware subgraph mixing to achieve superior few-shot performance with improved efficiency.
Modeling foot sinkage and slip with three extra degrees of freedom unlocks more accurate bipedal robot locomotion on challenging granular terrains.
A new foot-mud interaction model enables robots to walk more efficiently on mud by morphing their feet.
