  We present Label Anything, an innovative neural network architecture designed
for few-shot semantic segmentation (FSS) that demonstrates remarkable
generalizability across multiple classes with minimal examples required per
class. Diverging from traditional FSS methods that predominantly rely on masks
for annotating support images, Label Anything introduces varied visual prompts
-- points, bounding boxes, and masks -- thereby enhancing the framework's
versatility and adaptability. Unique to our approach, Label Anything is
engineered for end-to-end training across multi-class FSS scenarios,
efficiently learning from diverse support set configurations without
retraining. This approach enables a "universal" application to various FSS
challenges, ranging from $1$-way $1$-shot to complex $N$-way $K$-shot
configurations while remaining agnostic to the specific number of class
examples. This innovative training strategy reduces computational requirements
and substantially improves the model's adaptability and generalization across
diverse segmentation tasks. Our comprehensive experimental validation,
particularly achieving state-of-the-art results on the COCO-$20^i$ benchmark,
underscores Label Anything's robust generalization and flexibility. The source
code is publicly available at: https://github.com/pasqualedem/LabelAnything.
