One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks

• URL: http://arxiv.org/abs/2012.13690v1
• Date: Sat, 26 Dec 2020 07:40:00 GMT
• Title: One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks
• Authors: Sagar Gubbi Venkatesh and Bharadwaj Amrutur
• Abstract summary: In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments.
• Score: 0.7832189413179361
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments. We show that a simulated robot can pick-and-place novel objects pointed to by a laser pointer. We also evaluate the performance of the proposed approach on a dataset derived from the Omniglot handwritten character dataset and on a small dataset of toys.

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