Related papers: Open World Compositional Zero-Shot Learning

Open World Compositional Zero-Shot Learning

URL: http://arxiv.org/abs/2101.12609v1
Date: Fri, 29 Jan 2021 14:45:52 GMT
Title: Open World Compositional Zero-Shot Learning
Authors: Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata
Abstract summary: Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. We operate on the open world setting, where the search space includes a large number of unseen compositions.
Score: 47.09665742252187
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming the presence of prior knowledge about the unseen compositions, we operate on 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.

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