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Learning Conditional Attributes for Compositional Zero-Shot Learning

2023-05-29 · CVPR 2023 1 · Qingsheng Wang, Lingqiao Liu, Chenchen Jing, Hao Chen, Guoqiang Liang, Peng Wang, Chunhua Shen

Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute `wet" in wet apple" and `wet cat" is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes. Code is available at https://github.com/wqshmzh/CANet-CZSL

📄 PDF Abstract BibTeX arXiv:2305.17940

Code (1)

wqshmzh/canet-czsl 공식 구현 pytorch

Tasks

AttributeCompositional Zero-Shot Learning Zero-Shot Learning

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