  Convolutional Neural Networks (CNNs) achieve state-of-the-art 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.
