Detecting Semantic Parts on Partially Occluded Objects
Jianyu Wang, Zhishuai Zhang, Cihang Xie, Jun Zhu, Lingxi Xie and Alan Yuille
Abstract
In this paper, we address the task of detecting semantic parts on partially occluded
objects. We consider a scenario where the model is trained using non-occluded images
but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the
models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by
accumulating the confidence of local visual cues. Specifically, the method uses a simple
voting method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are derived by
clustering the internal states of deep networks. We evaluate our voting scheme on the
VehicleSemanticPart dataset with dense part annotations. We randomly place two, three
or four irrelevant objects onto the target object to generate testing images with various
occlusions.
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DOI
10.5244/C.31.73
https://dx.doi.org/10.5244/C.31.73
Citation
Jianyu Wang, Zhishuai Zhang, Cihang Xie, Jun Zhu, Lingxi Xie and Alan Yuille. Detecting Semantic Parts on Partially Occluded Objects. In T.K. Kim, S. Zafeiriou, G. Brostow and K. Mikolajczyk, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 73.1-73.13. BMVA Press, September 2017.
Bibtex
@inproceedings{BMVC2017_73,
title={Detecting Semantic Parts on Partially Occluded Objects},
author={Jianyu Wang, Zhishuai Zhang, Cihang Xie, Jun Zhu, Lingxi Xie and Alan Yuille},
year={2017},
month={September},
pages={73.1-73.13},
articleno={73},
numpages={13},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
doi={10.5244/C.31.73},
isbn={1-901725-60-X},
url={https://dx.doi.org/10.5244/C.31.73}
}