  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.
