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Matching Targets Across Domains with RADON, the Re-Identification Across Domain Network

2021-05-25 16:36:38
Cassandra Burgess, Cordelia Neisinger, Rafael Dinner
arXiv_CV
       
Abstract

We present a novel convolutional neural network that learns to match images of an object taken from different viewpoints or by different optical sensors. Our Re-Identification Across Domain Network (RADON) scores pairs of input images from different domains on similarity. Our approach extends previous work on Siamese networks and modifies them to more challenging use cases, including low- and no-shot learning, in which few images of a specific target are available for training. RADON shows strong performance on cross-view vehicle matching and cross-domain person identification in a no-shot learning environment.

Abstract (translated)

URL

https://arxiv.org/abs/2105.12056

PDF

https://arxiv.org/pdf/2105.12056.pdf

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