Current deep visual recognition systems suffer from severe performance
degradation when they encounter new images from classes and scenarios unseen
during training. Hence, the core challenge of Zero-Shot Learning (ZSL) is to
cope with the semantic-shift whereas the main challenge of Domain Adaptation
and Domain Generalization (DG) is the domain-shift. While historically ZSL and
DG tasks are tackled in isolation, this work develops with the ambitious goal
of solving them jointly,i.e. by recognizing unseen visual concepts in unseen
domains. We presentCuMix (CurriculumMixup for recognizing unseen categories in
unseen domains), a holistic algorithm to tackle ZSL, DG and ZSL+DG. The key
idea of CuMix is to simulate the test-time domain and semantic shift using
images and features from unseen domains and categories generated by mixing up
the multiple source domains and categories available during training. Moreover,
a curriculum-based mixing policy is devised to generate increasingly complex
training samples. Results on standard SL and DG datasets and on ZSL+DG using
the DomainNet benchmark demonstrate the effectiveness of our approach.

利用 CuMix 算法，结合 Zero-Shot Learning 和 Domain Adaptation 和 Domain Generalization，在识别未见过的可视概念和领域上大幅提升性能