  Compositional Zero-shot Learning (CZSL) aims to identify novel compositions
via known attribute-object pairs. The primary challenge in CZSL tasks lies in
the significant discrepancies introduced by the complex interaction between the
visual primitives of attribute and object, consequently decreasing the
classification performance towards novel compositions. Previous remarkable
works primarily addressed this issue by focusing on disentangling strategy or
utilizing object-based conditional probabilities to constrain the selection
space of attributes. Unfortunately, few studies have explored the problem from
the perspective of modeling the mechanism of visual primitive interactions.
Inspired by the success of vanilla adversarial learning in Cross-Domain
Few-Shot Learning, we take a step further and devise a model-agnostic and
Primitive-Based Adversarial training (PBadv) method to deal with this problem.
Besides, the latest studies highlight the weakness of the perception of hard
compositions even under data-balanced conditions. To this end, we propose a
novel over-sampling strategy with object-similarity guidance to augment target
compositional training data. We performed detailed quantitative analysis and
retrieval experiments on well-established datasets, such as UT-Zappos50K,
MIT-States, and C-GQA, to validate the effectiveness of our proposed method,
and the state-of-the-art (SOTA) performance demonstrates the superiority of our
approach. The code is available at https://github.com/lisuyi/PBadv_czsl.
