Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition

ICCV 2019 Kun Wei Muli Yang Hao Wang Cheng Deng Xianglong Liu

Recognizing unseen attribute-object pairs never appearing in the training data is a challenging task, since an object often refers to a specific entity while an attribute is an abstract semantic description. Besides, attributes are highly correlated to objects, i.e., an attribute tends to describe different visual features of various objects... (read more)

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