VitaTouch combines vision-tactile encoders with a dual Q-Former and contrastive alignment to an LLM, achieving 88.89% hardness and 75.13% roughness accuracy on a new 186-object dataset plus 94% success in robotic sorting trials.
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
VitaTouch: Property-Aware Vision-Tactile-Language Model for Robotic Quality Inspection in Manufacturing
VitaTouch combines vision-tactile encoders with a dual Q-Former and contrastive alignment to an LLM, achieving 88.89% hardness and 75.13% roughness accuracy on a new 186-object dataset plus 94% success in robotic sorting trials.