  Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions
using knowledge learned from seen attribute-object compositions in the training
set. Previous works mainly project an image and a composition into a common
embedding space to measure their compatibility score. However, both attributes
and objects share the visual representations learned above, leading the model
to exploit spurious correlations and bias towards seen pairs. Instead, we
reconsider CZSL as an out-of-distribution generalization problem. If an object
is treated as a domain, we can learn object-invariant features to recognize the
attributes attached to any object reliably. Similarly, attribute-invariant
features can also be learned when recognizing the objects with attributes as
domains. Specifically, we propose an invariant feature learning framework to
align different domains at the representation and gradient levels to capture
the intrinsic characteristics associated with the tasks. Experiments on two
CZSL benchmarks demonstrate that the proposed method significantly outperforms
the previous state-of-the-art.
