  Compositional Zero-Shot learning (CZSL) requires to recognize state-object
compositions unseen during training. In this work, instead of assuming prior
knowledge about the unseen compositions, we operate in the open world setting,
where the search space includes a large number of unseen compositions some of
which might be unfeasible. In this setting, we start from the cosine similarity
between visual features and compositional embeddings. After estimating the
feasibility score of each composition, we use these scores to either directly
mask the output space or as a margin for the cosine similarity between visual
features and compositional embeddings during training. Our experiments on two
standard CZSL benchmarks show that all the methods suffer severe performance
degradation when applied in the open world setting. While our simple CZSL model
achieves state-of-the-art performances in the closed world scenario, our
feasibility scores boost the performance of our approach in the open world
setting, clearly outperforming the previous state of the art.
