The model uses dense visuo-tactile feature interactions and material-diversity pairing on expanded datasets to generate tactile saliency maps for material segmentation, outperforming prior global-alignment methods.
In Table 6, we report material classification results ob- tained by training and evaluating on the original Touch- and-Go train-test split for fair comparison
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Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions
The model uses dense visuo-tactile feature interactions and material-diversity pairing on expanded datasets to generate tactile saliency maps for material segmentation, outperforming prior global-alignment methods.