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.
翻译:零热成份学习( CZSL) 旨在利用培训集中从可见属性对象构成中获取的知识来认识新构成。 先前的作品主要将图像和成份投射成一个共同嵌入空间, 以测量其兼容度。 但是, 属性和对象都使用上文所学的直观表达方式, 导致模型利用虚假的关联和对已见配对的偏向。 相反, 我们重新考虑CZSL( CZSL) 是一个分配外的概括问题。 如果将一个对象视为一个域, 我们可以学习对象变量特性, 以识别任何对象的属性。 同样, 在识别属性为域时, 也可以学习属性异性特征。 具体地说, 我们提议一个变量学习框架, 以在代表层面和梯度上对不同区域进行匹配, 以捕捉与任务相关的内在特征。 对两个 CZSLL 基准的实验表明, 拟议的方法大大超越了先前的状态 。