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• W2336302573 abstract "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." @default.
• W2336302573 created "2016-06-24" @default.
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• W2336302573 date "2016-04-08" @default.
• W2336302573 modified "2023-09-27" @default.
• W2336302573 title "CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples" @default.
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• W2336302573 hasPublicationYear "2016" @default.
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