@online{Mancini2105.01017,
TITLE = {Learning Graph Embeddings for Open World Compositional Zero-Shot Learning},
AUTHOR = {Mancini, Massimiliano and Naeem, Muhammad Ferjad and Xian, Yongqin and Akata, Zeynep},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2105.01017},
EPRINT = {2105.01017},
EPRINTTYPE = {arXiv},
YEAR = {2021},
ABSTRACT = {Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions<br>of state and object visual primitives seen during training. A problem with<br>standard CZSL is the assumption of knowing which unseen compositions will be<br>available at test time. In this work, we overcome this assumption operating on<br>the open world setting, where no limit is imposed on the compositional space at<br>test time, and the search space contains a large number of unseen compositions.<br>To address this problem, we propose a new approach, Compositional Cosine Graph<br>Embeddings (Co-CGE), based on two principles. First, Co-CGE models the<br>dependency between states, objects and their compositions through a graph<br>convolutional neural network. The graph propagates information from seen to<br>unseen concepts, improving their representations. Second, since not all unseen<br>compositions are equally feasible, and less feasible ones may damage the<br>learned representations, Co-CGE estimates a feasibility score for each unseen<br>composition, using the scores as margins in a cosine similarity-based loss and<br>as weights in the adjacency matrix of the graphs. Experiments show that our<br>approach achieves state-of-the-art performances in standard CZSL while<br>outperforming previous methods in the open world scenario.<br>},
}