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conference paper
Deep Feature Factorization for Concept Discovery
2018
Proceedings of the 15th European Conference on Computer Vision
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical cluster structures in feature space. This is visualized as heat maps, which highlight semantically matching regions across a set of images, revealing what the network 'perceives' as similar. DFF can also be used to perform co-segmentation and co-localization, and we report state-of-the-art results on these tasks.
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DFF_ECCV.pdf
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openaccess
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5.19 MB
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Adobe PDF
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