Primitive Generation and Semantic-Related Alignment for Universal Zero-Shot Segmentation
Primitive Generation and Semantic-Related Alignment for Universal Zero-Shot Segmentation
We study universal zero-shot segmentation in this work to achieve panoptic, instance, and semantic segmentation for novel categories without any training samples. Such zero-shot segmentation ability relies on inter-class relationships in semantic space to transfer the visual knowledge learned from seen categories to unseen ones. Thus, it is desired to …