Convolutional Neural Networks (CNNs) achieve state-of-theart performance in many computer vision tasks. However, this achievement is preceded by extreme manual annotation in order to perform either training from scratch or fine-tuning for the target task. In this work, we propose to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner. We employ state-of-the-art retrieval and Structure-from-Motion (SfM) methods to obtain 3D models, which are used to guide the selection of the training data for CNN fine-tuning. We show that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.
CITATION STYLE
Radenović, F., Tolias, G., & Chum, O. (2016). CNN image retrieval learns from bow: Unsupervised fine-tuning with hard examples. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9905 LNCS, pp. 3–20). Springer Verlag. https://doi.org/10.1007/978-3-319-46448-0_1
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