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

arXiv: https://arxiv.org/abs/2012.13690 · HTML: https://mlsanity.com/paper/2012.13690/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Neural Network Controller for Autonomous Pile Loading Revised](https://mlsanity.com/paper/2103.12379/) (2021)
- [One-Shot General Object Localization](https://mlsanity.com/paper/2211.13392/) (2022)

## Abstract (arXiv)

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.