Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions
of state and object visual primitives seen during training. A problem with
standard CZSL is the assumption of knowing which unseen compositions will be
available at test time. In this work, we overcome this assumption operating on
the open world setting, where no limit is imposed on the compositional space at
test time, and the search space contains a large number of unseen compositions.
To address this problem, we propose a new approach, Compositional Cosine Graph
Embeddings (Co-CGE), based on two principles. First, Co-CGE models the
dependency between states, objects and their compositions through a graph
convolutional neural network. The graph propagates information from seen to
unseen concepts, improving their representations. Second, since not all unseen
compositions are equally feasible, and less feasible ones may damage the
learned representations, Co-CGE estimates a feasibility score for each unseen
composition, using the scores as margins in a cosine similarity-based loss and
as weights in the adjacency matrix of the graphs. Experiments show that our
approach achieves state-of-the-art performances in standard CZSL while
outperforming previous methods in the open world scenario.

本文提出了一种基于图卷积神经网络的组合余弦图嵌入方法，通过估计每种未知组合的可行性得分，进而提高其在余弦相似度损失函数中的权重，并在开放世界场景中实现了最先进的表现。

学习图嵌入以进行开放世界组合零样本学习

Learning Graph Embeddings for Open World Compositional Zero-Shot  Learning

Compositional Zero-Shot learning (CZSL) requires to recognize state-object
compositions unseen during training. In this work, instead of assuming prior
knowledge about the unseen compositions, we operate in the open world setting,
where the search space includes a large number of unseen compositions some of
which might be unfeasible. In this setting, we start from the cosine similarity
between visual features and compositional embeddings. After estimating the
feasibility score of each composition, we use these scores to either directly
mask the output space or as a margin for the cosine similarity between visual
features and compositional embeddings during training. Our experiments on two
standard CZSL benchmarks show that all the methods suffer severe performance
degradation when applied in the open world setting. While our simple CZSL model
achieves state-of-the-art performances in the closed world scenario, our
feasibility scores boost the performance of our approach in the open world
setting, clearly outperforming the previous state of the art.

本文提出了一种新的模型用于 compositional zero-shot learning，该模型通过评估组合成分的可行性分数，采用余弦相似度来估计视觉特征和组成嵌入之间的相似度，从而实现解决开放世界中的 compositional zero-shot learning 问题的目标。在实验中，该方法表现出了显着的优越性能。