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Poster: Translating Vision into Words: Advancing Object Recognition with Visual-Language Models

Published: 04 June 2024 Publication History

Abstract

Poster: This study focuses on automatically identifying and classifying objects within indoor environments. Traditional methods struggle with this task due to the high cost of manually labeling each object and the inherent ambiguity of written descriptions. To overcome these limitations, we propose a novel instance segmentation approach that utilizes a visual-language model. This system is trained on extensive indoor environment data, including detailed point clouds (3D representations) and RGB images, readily collected by modern smartphone sensors. By eliminating the need for pre-defined labels, the system allows users to search for items using natural language. We evaluate the system's effectiveness through experiments analyzing performance metrics like accuracy and efficiency with public datasets, demonstrating its practicality and potential usefulness.

References

[1]
Y. Okochi et al., "Object recognition from 3d point cloud on resource-constrained edge device," in 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2022, pp. 369--374.
[2]
Y. Okochi, H. Rizk, and H. Yamaguchi, "On-the-fly spatio-temporal human segmentation of 3d point cloud data by micro-size lidar," in 2022 18th International Conference on Intelligent Environments (IE), 2022, pp. 1--4.
[3]
S. Peng et al., "Openscene: 3d scene understanding with open vocabularies," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[4]
A. Radford et al., "Learning transferable visual models from natural language supervision," in International conference on machine learning. PMLR, 2021, pp. 8748--8763.
[5]
H. Rizk et al., "Indoor localization system for seamless tracking across buildings and network configurations," in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 776--782.
[6]
J. Straub et al., "The Replica dataset: A digital replica of indoor spaces," arXiv preprint arXiv:1906.05797, 2019.

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  1. Poster: Translating Vision into Words: Advancing Object Recognition with Visual-Language Models

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      cover image ACM Conferences
      MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services
      June 2024
      778 pages
      ISBN:9798400705816
      DOI:10.1145/3643832
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 04 June 2024

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      Author Tags

      1. point cloud
      2. visual language model
      3. AIoT
      4. smart environments

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