Karaman, KaanGundogdu, ErhanKoc, AykutAlatan, A. Aydin2020-04-172020-04-172020-04-172019-01-0110.1109/ICIP.2019.8803401https://infoscience.epfl.ch/handle/20.500.14299/168227WOS:000521828603118Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by using a multi-task learning framework, in which the hierarchical labels (coarse and fine labels) of the samples are utilized both for classification and a quadruplet-based loss function. In order to improve the recognition strength of the learned features, we present a novel feature selection method specifically designed for four training samples of a quadruplet. By experiments, it is observed that the selection of very hard negative samples with relatively easy positive ones from the same coarse and fine classes significantly increases some performance metrics in a fine-grained dataset when compared to selecting the quadruplet samples randomly. The feature embedding learned by the proposed method achieves favorable performance against its state-of-the-art counterparts.deep distance metric learningembedding learningfine-grained classification/recognitiontext::conference output::conference proceedings::conference paper