UNIV introduces Patch Cross-modal Contrastive Learning (PCCL) to build a unified semantic feature space for infrared and visible modalities, supported by the new MVIP dataset of 98,992 aligned pairs, with reported gains on infrared segmentation and detection tasks.
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UNIV: Unified Foundation Model for Infrared and Visible Modalities
UNIV introduces Patch Cross-modal Contrastive Learning (PCCL) to build a unified semantic feature space for infrared and visible modalities, supported by the new MVIP dataset of 98,992 aligned pairs, with reported gains on infrared segmentation and detection tasks.