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Weakly Supervised Object Localization Using Things and Stuff Transfer

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Weakly Supervised Object Localization Using Things and Stuff Transfer. / Shi, Miaojing; Caesar, Holger ; Ferrari, Vittorio.

In: 2017 IEEE International Conference on Computer Vision (ICCV), Vol. 1, 2017, p. 3401-3410.

Research output: Contribution to journalConference paperpeer-review

Harvard

Shi, M, Caesar, H & Ferrari, V 2017, 'Weakly Supervised Object Localization Using Things and Stuff Transfer', 2017 IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 3401-3410. https://doi.org/10.1109/ICCV.2017.366

APA

Shi, M., Caesar, H., & Ferrari, V. (2017). Weakly Supervised Object Localization Using Things and Stuff Transfer. 2017 IEEE International Conference on Computer Vision (ICCV), 1, 3401-3410. https://doi.org/10.1109/ICCV.2017.366

Vancouver

Shi M, Caesar H, Ferrari V. Weakly Supervised Object Localization Using Things and Stuff Transfer. 2017 IEEE International Conference on Computer Vision (ICCV). 2017;1:3401-3410. https://doi.org/10.1109/ICCV.2017.366

Author

Shi, Miaojing ; Caesar, Holger ; Ferrari, Vittorio. / Weakly Supervised Object Localization Using Things and Stuff Transfer. In: 2017 IEEE International Conference on Computer Vision (ICCV). 2017 ; Vol. 1. pp. 3401-3410.

Bibtex Download

@article{2144071bc2674fa591e503d80fa717bf,
title = "Weakly Supervised Object Localization Using Things and Stuff Transfer",
abstract = "We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and target classes might share similar appearance (e.g. bear fur is similar to cat fur) or appear against similar background (e.g. horse and sheep appear against grass). To exploit this, we acquire three types of knowledge from the source set: a segmentation model trained on both thing and stuff classes; similarity relations between target and source classes; and cooccurrence relations between thing and stuff classes in the source. The segmentation model is used to generate thing and stuff segmentation maps on a target image, while the class similarity and co-occurrence knowledge help refining them. We then incorporate these maps as new cues into a multiple instance learning framework (MIL), propagating the transferred knowledge from the pixel level to the object proposal level. In extensive experiments, we conduct our transfer from the PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007 datasets (targets). We evaluate our transfer across widely different thing classes, including some that are not similar in appearance, but appear against similar background. The results demonstrate significant improvement over standard MIL, and we outperform the state-of-the-art in the transfer setting",
author = "Miaojing Shi and Holger Caesar and Vittorio Ferrari",
year = "2017",
doi = "10.1109/ICCV.2017.366",
language = "English",
volume = "1",
pages = "3401--3410",
journal = "2017 IEEE International Conference on Computer Vision (ICCV)",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Weakly Supervised Object Localization Using Things and Stuff Transfer

AU - Shi, Miaojing

AU - Caesar, Holger

AU - Ferrari, Vittorio

PY - 2017

Y1 - 2017

N2 - We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and target classes might share similar appearance (e.g. bear fur is similar to cat fur) or appear against similar background (e.g. horse and sheep appear against grass). To exploit this, we acquire three types of knowledge from the source set: a segmentation model trained on both thing and stuff classes; similarity relations between target and source classes; and cooccurrence relations between thing and stuff classes in the source. The segmentation model is used to generate thing and stuff segmentation maps on a target image, while the class similarity and co-occurrence knowledge help refining them. We then incorporate these maps as new cues into a multiple instance learning framework (MIL), propagating the transferred knowledge from the pixel level to the object proposal level. In extensive experiments, we conduct our transfer from the PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007 datasets (targets). We evaluate our transfer across widely different thing classes, including some that are not similar in appearance, but appear against similar background. The results demonstrate significant improvement over standard MIL, and we outperform the state-of-the-art in the transfer setting

AB - We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and target classes might share similar appearance (e.g. bear fur is similar to cat fur) or appear against similar background (e.g. horse and sheep appear against grass). To exploit this, we acquire three types of knowledge from the source set: a segmentation model trained on both thing and stuff classes; similarity relations between target and source classes; and cooccurrence relations between thing and stuff classes in the source. The segmentation model is used to generate thing and stuff segmentation maps on a target image, while the class similarity and co-occurrence knowledge help refining them. We then incorporate these maps as new cues into a multiple instance learning framework (MIL), propagating the transferred knowledge from the pixel level to the object proposal level. In extensive experiments, we conduct our transfer from the PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007 datasets (targets). We evaluate our transfer across widely different thing classes, including some that are not similar in appearance, but appear against similar background. The results demonstrate significant improvement over standard MIL, and we outperform the state-of-the-art in the transfer setting

U2 - 10.1109/ICCV.2017.366

DO - 10.1109/ICCV.2017.366

M3 - Conference paper

VL - 1

SP - 3401

EP - 3410

JO - 2017 IEEE International Conference on Computer Vision (ICCV)

JF - 2017 IEEE International Conference on Computer Vision (ICCV)

ER -

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