Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning
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Date
2021
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Citation of Original Publication
A. T. Nguyen et al., "Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, pp. 1613-1622, doi: 10.1109/CVPRW53098.2021.00177.
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Abstract
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts
of real-world items. Our approach learns these embeddings
by sampling triples of anchor, positive, and negative data
points from RGB-depth images and their natural language
descriptions. We show that our approach can benefit from,
but does not require, post-processing steps such as Procrustes analysis, in contrast to some of our baselines which
require it for reasonable performance. We demonstrate the
effectiveness of our approach on two datasets commonly
used to develop robotic-based grounded language learning
systems, where our approach outperforms four baselines,
including a state-of-the-art approach, across five evaluation
metrics.