Deep Feature Factorization For Concept Discovery

Edo Collins; Radhakrishna Achanta; Sabine Susstrunk

ECCV 2018poster118 citations

Deep Feature Factorization For Concept Discovery

Edo Collins, Radhakrishna Achanta, Sabine Susstrunk

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.

BibTeX

@inproceedings{eccv2018_deepfeaturefacto,
  title = {Deep Feature Factorization For Concept Discovery},
  author = {Edo Collins and Radhakrishna Achanta and Sabine Susstrunk},
  booktitle = {ECCV 2018},
  year = {2018}
}