{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fine-tuning-cnn-image-retrieval-with-no-human","title":"Fine-tuning CNN Image Retrieval with No Human Annotation","arxiv_id":"1711.02512","date":"2017-11-03","proceeding":null,"authors":["Filip Radenović","Giorgos Tolias","Ondřej Chum"],"abstract":"Image descriptors based on activations of Convolutional Neural Networks\n(CNNs) have become dominant in image retrieval due to their discriminative\npower, compactness of representation, and search efficiency. Training of CNNs,\neither from scratch or fine-tuning, requires a large amount of annotated data,\nwhere a high quality of annotation is often crucial. In this work, we propose\nto fine-tune CNNs for image retrieval on a large collection of unordered images\nin a fully automated manner. Reconstructed 3D models obtained by the\nstate-of-the-art retrieval and structure-from-motion methods guide the\nselection of the training data. We show that both hard-positive and\nhard-negative examples, selected by exploiting the geometry and the camera\npositions available from the 3D models, enhance the performance of\nparticular-object retrieval. CNN descriptor whitening discriminatively learned\nfrom the same training data outperforms commonly used PCA whitening. We propose\na novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and\naverage pooling and show that it boosts retrieval performance. Applying the\nproposed method to the VGG network achieves state-of-the-art performance on the\nstandard benchmarks: Oxford Buildings, Paris, and Holidays datasets.","url_abs":"http://arxiv.org/abs/1711.02512v2","url_pdf":"http://arxiv.org/pdf/1711.02512v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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