Deep Feature Factorization for Concept Discovery
Abstract
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.
Cite
Text
Collins et al. "Deep Feature Factorization for Concept Discovery." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01264-9_21Markdown
[Collins et al. "Deep Feature Factorization for Concept Discovery." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/collins2018eccv-deep/) doi:10.1007/978-3-030-01264-9_21BibTeX
@inproceedings{collins2018eccv-deep,
title = {{Deep Feature Factorization for Concept Discovery}},
author = {Collins, Edo and Achanta, Radhakrishna and Susstrunk, Sabine},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01264-9_21},
url = {https://mlanthology.org/eccv/2018/collins2018eccv-deep/}
}