SWAN is the first adaptive multimodal network that meets variable compute budgets, optimizes layer use by sample complexity, and drops irrelevant features, cutting FLOPs up to 49% in 3D object detection with minimal accuracy loss.
arXiv preprint arXiv:2203.11496 (2022)
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GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flow matching.
citing papers explorer
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SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
SWAN is the first adaptive multimodal network that meets variable compute budgets, optimizes layer use by sample complexity, and drops irrelevant features, cutting FLOPs up to 49% in 3D object detection with minimal accuracy loss.
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GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
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InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flow matching.