  Existing few-shot segmentation (FSS) methods mainly focus on prototype
feature generation and the query-support matching mechanism. As a crucial
prompt for generating prototype features, the pair of image-mask types in the
support set has become the default setting. However, various types such as
image, text, box, and mask all can provide valuable information regarding the
objects in context, class, localization, and shape appearance. Existing work
focuses on specific combinations of guidance, leading FSS into different
research branches. Rethinking guidance types in FSS is expected to explore the
efficient joint representation of the coupling between the support set and
query set, giving rise to research trends in the weakly or strongly annotated
guidance to meet the customized requirements of practical users. In this work,
we provide the generalized FSS with seven guidance paradigms and develop a
universal vision-language framework (UniFSS) to integrate prompts from text,
mask, box, and image. Leveraging the advantages of large-scale pre-training
vision-language models in textual and visual embeddings, UniFSS proposes
high-level spatial correction and embedding interactive units to overcome the
semantic ambiguity drawbacks typically encountered by pure visual matching
methods when facing intra-class appearance diversities. Extensive experiments
show that UniFSS significantly outperforms the state-of-the-art methods.
Notably, the weakly annotated class-aware box paradigm even surpasses the
finely annotated mask paradigm.
