This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
Guoqing Chao, Shiliang Sun, and Jinbo Bi
2 Pith papers cite this work. Polarity classification is still indexing.
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Grace-BEV enables graceful degradation in BEV perception under sensor failures by using a TrustGate Router for modality trustworthiness and FailSafe Fusion Block for dynamic integration, with modality dropout training, restoring performance to 34.7% mAP under LiDAR failure on nuScenes variants.
citing papers explorer
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Deep Multimodal Learning with Missing Modality: A Survey
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
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Can BEV Perception Gracefully Degrade under Sensor Failures?
Grace-BEV enables graceful degradation in BEV perception under sensor failures by using a TrustGate Router for modality trustworthiness and FailSafe Fusion Block for dynamic integration, with modality dropout training, restoring performance to 34.7% mAP under LiDAR failure on nuScenes variants.