Visualizing Deep Similarity Networks
Abby Stylianou, Richard Souvenir, Robert Pless . 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) 2019 – 5 citations
For convolutional neural network models that optimize an image embedding, we propose 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. We also generalize our approach to embedding networks that use different pooling strategies and provide a simple mechanism to support image similarity searches on objects or sub-regions in the query image.