NAACL 2021long21 citations

OCID-Ref: A 3D Robotic Dataset With Embodied Language For Clutter Scene Grounding

Ke-Jyun Wang, Yun-Hsuan Liu, Hung-Ting Su, Jen-Wei Wang, Yu-Siang Wang, Winston Hsu, Wen-Chin Chen

Abstract

To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects. However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded due to space utilization issues. In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects. OCID-Ref consists of 305,694 referring expressions from 2,300 scenes with providing RGB image and point cloud inputs. To resolve challenging occlusion issues, we argue that it’s crucial to take advantage of both 2D and 3D signals to resolve challenging occlusion issues. Our experimental results demonstrate the effectiveness of aggregating 2D and 3D signals but referring to occluded objects still remains challenging for the modern visual grounding systems. OCID-Ref is publicly available at https://github.com/lluma/OCID-Ref

BibTeX
@inproceedings{wang-etal-2021-ocid,
    title = "{OCID}-Ref: A 3{D} Robotic Dataset With Embodied Language For Clutter Scene Grounding",
    author = "Wang, Ke-Jyun  and
      Liu, Yun-Hsuan  and
      Su, Hung-Ting  and
      Wang, Jen-Wei  and
      Wang, Yu-Siang  and
      Hsu, Winston  and
      Chen, Wen-Chin",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.419/",
    doi = "10.18653/v1/2021.naacl-main.419",
    pages = "5333--5338"
}