On learning to localize objects with minimal supervision

Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell
; Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1611-1619, 2014.

Abstract

Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation. The latter allows us to leverage efficient quasi-Newton optimization techniques. Our experiments demonstrate that the proposed approach provides a 50% relative improvement in mean average precision over the current state-of-the-art on PASCAL VOC 2007 detection.

Cite this Paper


BibTeX
@InProceedings{pmlr-v32-songb14, title = {On learning to localize objects with minimal supervision}, author = {Hyun Oh Song and Ross Girshick and Stefanie Jegelka and Julien Mairal and Zaid Harchaoui and Trevor Darrell}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1611--1619}, year = {2014}, editor = {Eric P. Xing and Tony Jebara}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/songb14.pdf}, url = {http://proceedings.mlr.press/v32/songb14.html}, abstract = {Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation. The latter allows us to leverage efficient quasi-Newton optimization techniques. Our experiments demonstrate that the proposed approach provides a 50% relative improvement in mean average precision over the current state-of-the-art on PASCAL VOC 2007 detection.} }
Endnote
%0 Conference Paper %T On learning to localize objects with minimal supervision %A Hyun Oh Song %A Ross Girshick %A Stefanie Jegelka %A Julien Mairal %A Zaid Harchaoui %A Trevor Darrell %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-songb14 %I PMLR %J Proceedings of Machine Learning Research %P 1611--1619 %U http://proceedings.mlr.press %V 32 %N 2 %W PMLR %X Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation. The latter allows us to leverage efficient quasi-Newton optimization techniques. Our experiments demonstrate that the proposed approach provides a 50% relative improvement in mean average precision over the current state-of-the-art on PASCAL VOC 2007 detection.
RIS
TY - CPAPER TI - On learning to localize objects with minimal supervision AU - Hyun Oh Song AU - Ross Girshick AU - Stefanie Jegelka AU - Julien Mairal AU - Zaid Harchaoui AU - Trevor Darrell BT - Proceedings of the 31st International Conference on Machine Learning PY - 2014/01/27 DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-songb14 PB - PMLR SP - 1611 DP - PMLR EP - 1619 L1 - http://proceedings.mlr.press/v32/songb14.pdf UR - http://proceedings.mlr.press/v32/songb14.html AB - Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation. The latter allows us to leverage efficient quasi-Newton optimization techniques. Our experiments demonstrate that the proposed approach provides a 50% relative improvement in mean average precision over the current state-of-the-art on PASCAL VOC 2007 detection. ER -
APA
Song, H.O., Girshick, R., Jegelka, S., Mairal, J., Harchaoui, Z. & Darrell, T.. (2014). On learning to localize objects with minimal supervision. Proceedings of the 31st International Conference on Machine Learning, in PMLR 32(2):1611-1619

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