Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions
formed from seen state and object during training. Since the same state may be
various in the visual appearance while entangled with different objects, CZSL
is still a challenging task. Some methods recognize state and object with two
trained classifiers, ignoring the impact of the interaction between object and
state; the other methods try to learn the joint representation of the
state-object compositions, leading to the domain gap between seen and unseen
composition sets. In this paper, we propose a novel Siamese Contrastive
Embedding Network (SCEN) (Code: this https URL) for
unseen composition recognition. Considering the entanglement between state and
object, we embed the visual feature into a Siamese Contrastive Space to capture
prototypes of them separately, alleviating the interaction between state and
object. In addition, we design a State Transition Module (STM) to increase the
diversity of training compositions, improving the robustness of the recognition
model. Extensive experiments indicate that our method significantly outperforms
the state-of-the-art approaches on three challenging benchmark datasets,
including the recent proposed C-QGA dataset.

本文提出了一种新颖的 Siamese 对比嵌入网络（SCEN）方法和状态转移模块（STM），用于解决复杂情况下的无监督学习任务，实验表明该方法在三个基准数据集上均大幅优于现有方法。