DAR-Net applies transformer temporal reasoning with pixel-level semantic supervision to classify six diver activities on a new 2,600-image UDA dataset, reporting better accuracy than prior models in controlled tests.
Visual Detection of Diver Atten- tiveness for Underwater Human-Robot Interaction,
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Semantically-Aware Diver Activity Recognition Framework for Effective Underwater Multi-Human-Robot Collaboration
DAR-Net applies transformer temporal reasoning with pixel-level semantic supervision to classify six diver activities on a new 2,600-image UDA dataset, reporting better accuracy than prior models in controlled tests.