Cooperative Training of Triplet Networks for Cross-Domain Matching
Giovanni G. De Giacomo, Matheus M. dos Santos, Paulo Drews, Sílvia Silva da Costa Botelho · 2020
Recently, Deep Convolutional Neural Networks have been applied to various computer vision problems and achieved state-of-the-art results. Among these, Siamese and Triplet networks have obtained great traction in intra-domain matching. However, it is impossible to directly use these networks in cross-domain problems. Thus, this paper proposes a data-driven approach for cross-domain matching of complex data that do not share similar features. A pair of triplet networks are trained with a new cooperative approach to perform Deep Metric Learning. In order to validate our proposed method, we apply it to a cross-domain image matching problem that aims to assist with underwater robot localization. We train a pair of networks using our methodology on a dataset composed of acoustic and segmented aerial images and evaluate it on a dataset acquired in another location. Our results show that our method is able to achieve up to 83% accuracy in matching acoustic and segmented aerial images.