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Visualizing Deep Similarity Networks

NCJ Number
2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Dated: 2019 Pages: 2029-2037
Abby Stylianou; Richard Souvenir; Robert Pless
Date Published
9 pages

For convolutional neural network models that optimize an image embedding, this article proposes a method to highlight the regions of images that contribute most to pairwise similarity.


This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to similarity learning. The visualization shows how similarity networks that are fine-tuned learn to focus on different features. This approach is generalized to embedding networks that use different pooling strategies and provides a simple mechanism to support image similarity searches on objects or sub-regions in the query image. (publisher abstract modified)