Fine-grained object recognition and zero-shot learning in multispectral imagery

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2018-05-05
Sumbul, Gencer
Cinbiş, Ramazan Gökberk
AKSOY, SELİM
We present a method for fine-grained object recognition problem, that aims to recognize the type of an object among a large number of sub-categories, and zero-shot learning scenario on multispectral images. In order to establish a relation between seen classes and new unseen classes, a compatibility function between image features extracted from a convolutional neural network and auxiliary information of classes is learnt. Knowledge transfer for unseen classes is carried out by maximizing this function. Performance of the model (15.2%) evaluated with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information is promisingly better than the other methods for 16 test classes.

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Citation Formats
G. Sumbul, R. G. Cinbiş, and S. AKSOY, “Fine-grained object recognition and zero-shot learning in multispectral imagery,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/44776.