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.
In: European conference on computer vision
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
BoxerNet lifts 2D bounding boxes to metric 3D boxes via transformer regression with aleatoric uncertainty and median depth encoding, then fuses multi-view results to outperform CuTR by large margins on open-world benchmarks.
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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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Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D
BoxerNet lifts 2D bounding boxes to metric 3D boxes via transformer regression with aleatoric uncertainty and median depth encoding, then fuses multi-view results to outperform CuTR by large margins on open-world benchmarks.