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Generalized Contrastive Optimization of Siamese Networks for Place Recognition by Leyva-Vallina, María; Strisciuglio, Nicola; Petkov, Nicolai is a scholarly article available to read on EtoBox.

What is Generalized Contrastive Optimization of Siamese Networks for Place Recognition about?

Visual place recognition is a challenging task in computer vision and a key component of camera-based localization and navigation systems. Recently, Convolutional Neural Networks (CNNs) achieved high results and good generalization capabilities. They are usually trained using pairs or triplets of images labeled as either similar or dissimilar, in a binary fashion. In practice, the similarity between two images is not binary, but continuous. Furthermore, training these CNNs is computationally complex and involves costly pair and triplet mining strategies. We propose a Generalized Contrastive loss (GCL) function that relies on image similarity as a continuous measure, and use it to train a siamese CNN. Furthermore, we present three techniques for automatic annotation of image pairs with labels indicating their degree of similarity, and deploy them to re-annotate the MSLS, TB-Places, and 7Scenes datasets. We demonstrate that siamese CNNs trained using the GCL function and the improved annotations consistently outperform their binary counterparts. Our models trained on MSLS outperform the state-of-the-art methods, including NetVLAD, NetVLAD-SARE, AP-GeM and Patch-NetVLAD, and generaliz

Author

Leyva-Vallina, María; Strisciuglio, Nicola; Petkov, Nicolai

Published

2021

Language

EN