Voxel-informed Language Grounding

Rodolfo Corona; Shizhan Zhu; Dan Klein; Trevor Darrell

ACL 2022short13 citations

Voxel-informed Language Grounding

Rodolfo Corona, Shizhan Zhu, Dan Klein, Trevor Darrell

Abstract

Natural language applied to natural 2D images describes a fundamentally 3D world. We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model. We show that VLG significantly improves grounding accuracy on SNARE, an object reference game task. At the time of writing, VLG holds the top place on the SNARE leaderboard, achieving SOTA results with a 2.0% absolute improvement.

BibTeX

@inproceedings{corona-etal-2022-voxel,
    title = "Voxel-informed Language Grounding",
    author = "Corona, Rodolfo  and
      Zhu, Shizhan  and
      Klein, Dan  and
      Darrell, Trevor",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.7/",
    doi = "10.18653/v1/2022.acl-short.7",
    pages = "54--60"
}