Fig. 1. Pipelines of classical GFSS works and our Syn4Seg framework.
(a) Classical GFSS methods follow three stages: base class learning, novel
class registration, and evaluation, where novel classes rely on limited manual
annotations, resulting in low diversity and constrained performance. (b) Our
Syn4Seg framework synthesizes diverse, realistic novel class images and
pseudo labels guided by support annotations. These synthesized samples,
together with base data, are used to train a semantic segmentation model,
enabling robust recognition of both base and novel classes during evaluation.