@inproceedings{a1e2a816db464988b9bf81aa06816c11,
title = "DOOBNet: Deep Object Occlusion Boundary Detection from an Image",
abstract = "Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. Solving this problem is challenging as we encounter extreme boundary/non-boundary class imbalance during the training of an object occlusion boundary detector. In this paper, we propose to address this class imbalance by up-weighting the loss contribution of false negative and false positive examples with our novel Attention Loss function. We also propose a unified end-to-end multi-task deep object occlusion boundary detection network (DOOBNet) by sharing convolutional features to simultaneously predict object boundary and occlusion orientation. DOOBNet adopts an encoder-decoder structure with skip connection in order to automatically learn multi-scale and multi-level features. We significantly surpass the state-of-the-art on the PIOD dataset (ODS F-score of.702) and the BSDS ownership dataset (ODS F-score of.555), as well as improving the detecting speed to as 0.037{\^A} s per image on the PIOD dataset.",
keywords = "Boundary detection, Convolutional neural network, Occlusion reasoning",
author = "Guoxia Wang and Xiaochuan Wang and Li, \{Frederick W.B.\} and Xiaohui Liang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th Asian Conference on Computer Vision, ACCV 2018 ; Conference date: 02-12-2018 Through 06-12-2018",
year = "2019",
doi = "10.1007/978-3-030-20876-9\_43",
language = "英语",
isbn = "9783030208752",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "686--702",
editor = "Greg Mori and Hongdong Li and C.V. Jawahar and Konrad Schindler",
booktitle = "Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers",
address = "德国",
}