The goal of open-world compositional zero-shot learning (OW-CZSL) is to
recognize compositions of state and objects in images, given only a subset of
them during training and no prior on the unseen compositions. In this setting,
models operate on a huge output space, containing all possible state-object
compositions. While previous works tackle the problem by learning embeddings
for the compositions jointly, here we revisit a simple CZSL baseline and
predict the primitives, i.e. states and objects, independently. To ensure that
the model develops primitive-specific features, we equip the state and object
classifiers with separate, non-linear feature extractors. Moreover, we estimate
the feasibility of each composition through external knowledge, using this
prior to remove unfeasible compositions from the output space. Finally, we
propose a new setting, i.e. CZSL under partial supervision (pCZSL), where
either only objects or state labels are available during training, and we can
use our prior to estimate the missing labels. Our model, Knowledge-Guided
Simple Primitives (KG-SP), achieves state of the art in both OW-CZSL and pCZSL,
surpassing most recent competitors even when coupled with semi-supervised
learning techniques. Code available at: this https URL

本文研究 open-world compositional zero-shot learning 领域，提出了一种基于 primitive 预测、使用 external knowledge 估计可行性的模型 KG-SP，并引入 CZSL under partial supervision 的新设置，实现了优于竞争对手的表现。