In compositional zero-shot learning, the goal is to recognize unseen
compositions (e.g. old dog) of observed visual primitives states (e.g. old,
cute) and objects (e.g. car, dog) in the training set. This is challenging
because the same state can for example alter the visual appearance of a dog
drastically differently from a car. As a solution, we propose a novel graph
formulation called Compositional Graph Embedding (CGE) that learns image
features, compositional classifiers, and latent representations of visual
primitives in an end-to-end manner. The key to our approach is exploiting the
dependency between states, objects, and their compositions within a graph
structure to enforce the relevant knowledge transfer from seen to unseen
compositions. By learning a joint compatibility that encodes semantics between
concepts, our model allows for generalization to unseen compositions without
relying on an external knowledge base like WordNet. We show that in the
challenging generalized compositional zero-shot setting our CGE significantly
outperforms the state of the art on MIT-States and UT-Zappos. We also propose a
new benchmark for this task based on the recent GQA dataset. Code is available
at: this https URL

本研究提出了一种名为 Compositional Graph Embedding（CGE）的图形学习方法，可以实现对已出现的视觉对象和视觉基元状态的组合的识别和理解，进而实现了基于组合的零样本学习，避免了外部知识库的依赖。CGE 不仅可以学习图像特征，合成分类器，还可以学习视觉基元的潜在特征，最终可以进行对未出现过的组合图像识别，相比其他方法，CGE 在 MIT-States 和 UT-Zappos 等数据集上具有更好的性能表现。

学习图嵌入实现组合式零样本学习

Learning Graph Embeddings for 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。