CoVLA, built from standard cross-modal attention and gated fusion, is claimed to improve semantic location prediction accuracy by 2.3% and F1 by 2.4% over the SG-MFT baseline, despite missing error bars, code, and key experimental details.
Towards effective next POI prediction: Spatial and semantic augmentation wit h remote sensing data
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Dynamic Cross-Modal Alignment for Robust Semantic Location Prediction
CoVLA, built from standard cross-modal attention and gated fusion, is claimed to improve semantic location prediction accuracy by 2.3% and F1 by 2.4% over the SG-MFT baseline, despite missing error bars, code, and key experimental details.