BDATP enhances generalization in audio-visual navigation by explicitly modeling interaural differences and using auxiliary action prediction, achieving up to 21.6 percentage point gains in success rate on unheard sounds in Replica dataset.
Matterport3d: Learning from rgb-d data in indoor environments
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RAVN improves audio-visual navigation by learning audio-derived reliability cues via an Acoustic Geometry Reasoner and using them to modulate visual features through Reliability-Aware Geometric Modulation.
SACF discretizes target direction and distance from audio-visual cues then applies conditioned fusion to improve navigation efficiency and generalization to unheard sounds.
citing papers explorer
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Generalizable Audio-Visual Navigation via Binaural Difference Attention and Action Transition Prediction
BDATP enhances generalization in audio-visual navigation by explicitly modeling interaural differences and using auxiliary action prediction, achieving up to 21.6 percentage point gains in success rate on unheard sounds in Replica dataset.
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Reliability-Aware Geometric Fusion for Robust Audio-Visual Navigation
RAVN improves audio-visual navigation by learning audio-derived reliability cues via an Acoustic Geometry Reasoner and using them to modulate visual features through Reliability-Aware Geometric Modulation.
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Spatial-Aware Conditioned Fusion for Audio-Visual Navigation
SACF discretizes target direction and distance from audio-visual cues then applies conditioned fusion to improve navigation efficiency and generalization to unheard sounds.