@inproceedings{3b16edb7b9b546d29a9e0b5a72bf1c04,
title = "Towards generalizable distance estimation by leveraging graph information",
abstract = "Approximating the distance of objects present in an image remains an important problem for computer vision applications. Current SOTA methods rely on formulating this problem to convenience depth estimation at every pixel; however, there are limitations that make such solutions non-generalizable (i.e varying focal length). To address this issue, we propose reformulating distance approximation to a per-object detection problem and leveraging graph information extracted from the image to potentially achieve better generalizability on data acquired at multiple focal lengths.",
keywords = "Distance estimation, GCN",
author = "Cava, {John Kevin} and Todd Houghton and Hongbin Yu",
year = "2019",
month = oct,
doi = "10.1109/ICCVW.2019.00565",
language = "English (US)",
series = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4603--4605",
booktitle = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",
note = "17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 ; Conference date: 27-10-2019 Through 28-10-2019",
}