Sümbül, GencerAksoy, SelimCinbiş, R. G.2019-02-212019-02-2120189781538615010http://hdl.handle.net/11693/50215Date of Conference: 2-5 May 2018We 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.TurkishFine-grained classificationObject recognitionZero-shot learningConference Paper10.1109/SIU.2018.8404256