Deep Feature Factorization For Concept Discovery

Edo Collins, Radhakrishna Achanta, Sabine Susstrunk; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 336-352


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.

Related Material

[pdf] [arXiv]
author = {Collins, Edo and Achanta, Radhakrishna and Susstrunk, Sabine},
title = {Deep Feature Factorization For Concept Discovery},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}