  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 well bridge
semantic-visual spaces and apply the semantic relationships to visual feature
learning. We introduce a generative model to synthesize features for unseen
categories, which links semantic and visual spaces as well as addresses the
issue of lack of unseen training data. Furthermore, to mitigate the domain gap
between semantic and visual spaces, firstly, we enhance the vanilla generator
with learned primitives, each of which contains fine-grained attributes related
to categories, and synthesize unseen features by selectively assembling these
primitives. Secondly, we propose to disentangle the visual feature into the
semantic-related part and the semantic-unrelated part that contains useful
visual classification clues but is less relevant to semantic representation.
The inter-class relationships of semantic-related visual features are then
required to be aligned with those in semantic space, thereby transferring
semantic knowledge to visual feature learning. The proposed approach achieves
impressively state-of-the-art performance on zero-shot panoptic segmentation,
instance segmentation, and semantic segmentation. Code is available at
https://henghuiding.github.io/PADing/.
