Feature Recalibration Based Olfactory-Visual Multimodal Model for Enhanced Rice Deterioration Detection

ArXi:2602.14408v2 Announce Type: replace-cross Multimodal methods are widely used in rice deterioration detection, but they exhibit limited capability in representing and extracting fine-grained abnormal features. Moreover, these methods rely on devices such as hyperspectral cameras and mass spectrometers, which increase detection costs and prolong data acquisition time. To address these issues, we propose a feature recalibration based olfactory-visual multimodal model for enhanced rice deterioration detection.