Compositional zero-shot learning (CZSL) refers to recognizing unseen compositions of known visual primitives, which is an essential ability for artificial intelligence systems to learn and understand the world. While considerable progress has been made on existing benchmarks, we suspect whether popular CZSL methods can address the challenges of few-shot and few referential compositions, which is common when learning in real-world unseen environments. To this end, we study the challenging reference-limited compositional zero-shot learning (RL-CZSL) problem in this paper, i.e. , given limited seen compositions that contain only a few samples as reference, unseen compositions of observed primitives should be identified. We propose a novel Meta Compositional Graph Learner (MetaCGL) that can efficiently learn the compositionality from insufficient referential information and generalize to unseen compositions. Besides, we build a benchmark with two new large-scale datasets that consist of natural images with diverse compositional labels, providing more realistic environments for RL-CZSL. Extensive experiments in the benchmarks show that our method achieves state-of-the-art performance in recognizing unseen compositions when reference is limited for compositional learning.

翻译：零光成份学习(CZSL) 是指承认已知视觉原始学的无形构成,这是人工智能系统学习和理解世界的基本能力。虽然在现有的基准方面已经取得了相当大的进展,但我们怀疑流行的CZSL方法是否能够应对在现实世界的隐形环境中学习时常见的微光和很少优惠成份的挑战。为此,我们研究了本文件中具有挑战性的有限参考成份零光学习(RL-CZSL)问题,即,由于所见成份有限,仅包含少量样本作为参考,因此应当确定所观测到的原始学系的无形成份。我们提出了一个新的新颖的Meta 成份图学习器(MetaCGL),它能够有效地从不充分的优惠信息中学习成份,并概括到看不见的成份。此外,我们用两个新的大型数据集构建了一个基准,由具有不同组成标签的自然图像组成,为RL-CZSL提供更现实的环境。在基准中进行的广泛实验表明,我们的方法在确认不可见的成份时,在认识的成份方面实现了什么。