CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples

author: Filip Radenović, Department of Cybernetics, Czech Technical University in Prague
published: Oct. 24, 2016,   recorded: October 2016,   views: 211
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Description

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.

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Download slides icon Download slides: eccv2016_radenovic_cnn_image_01.pdf (3.2 MB)


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