  Addressing Out-Of-Distribution (OOD) Segmentation and Zero-Shot Semantic
Segmentation (ZS3) is challenging, necessitating segmenting unseen classes.
Existing strategies adapt the class-agnostic Mask2Former (CA-M2F) tailored to
specific tasks. However, these methods cater to singular tasks, demand training
from scratch, and we demonstrate certain deficiencies in CA-M2F, which affect
performance. We propose the Class-Agnostic Structure-Constrained Learning
(CSL), a plug-in framework that can integrate with existing methods, thereby
embedding structural constraints and achieving performance gain, including the
unseen, specifically OOD, ZS3, and domain adaptation (DA) tasks. There are two
schemes for CSL to integrate with existing methods (1) by distilling knowledge
from a base teacher network, enforcing constraints across training and
inference phrases, or (2) by leveraging established models to obtain per-pixel
distributions without retraining, appending constraints during the inference
phase. We propose soft assignment and mask split methodologies that enhance OOD
object segmentation. Empirical evaluations demonstrate CSL's prowess in
boosting the performance of existing algorithms spanning OOD segmentation, ZS3,
and DA segmentation, consistently transcending the state-of-art across all
three tasks.
