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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

TS\(^{2}\)C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

verfasst von : Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, Jinjun Xiong, Jiashi Feng, Thomas Huang

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C). We observe that object candidates mined through current multiple instance learning methods are usually trapped to discriminative object parts, rather than the entire object. TS2C leverages surrounding segmentation context derived from weakly-supervised segmentation to suppress such low-quality distracting candidates and boost the high-quality ones. Specifically, TS2C is developed based on two key properties of desirable bounding boxes: (1) high purity, meaning most pixels in the box are with high object response, and (2) high completeness, meaning the box covers high object response pixels comprehensively. With such novel and computable criteria, more tight candidates can be discovered for learning a better object detector. With TS2C, we obtain 48.0% and 44.4% mAP scores on VOC 2007 and 2012 benchmarks, which are the new state-of-the-arts.

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Literatur
1.
Bilen, H., Pedersoli, M., Tuytelaars, T.: Weakly supervised object detection with posterior regularization. In: BMVC, pp. 1–12 (2014)
2.
Bilen, H., Pedersoli, M., Tuytelaars, T.: Weakly supervised object detection with convex clustering. In: IEEE CVPR, pp. 1081–1089 (2015)
3.
Bilen, H., Vedaldi, A.: Weakly supervised deep detection networks. In: IEEE CVPR, pp. 2846–2854 (2016)
4.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. preprint arXiv:​1412.​7062 (2014)
5.
Chen, X., et al.: 3D object proposals for accurate object class detection. In: NIPS, pp. 424–432 (2015)
6.
Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., Huang, T.: Revisiting RCNN: on awakening the classification power of faster RCNN. In: ECCV (2018)
7.
Cinbis, R.G., Verbeek, J., Schmid, C.: Weakly supervised object localization with multi-fold multiple instance learning. IEEE TPAMI 39(1), 189–203 (2017)CrossRef
8.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE CVPR, pp. 248–255 (2009)
9.
Deselaers, T., Alexe, B., Ferrari, V.: Weakly supervised localization and learning with generic knowledge. IJCV 100(3), 275–293 (2012)MathSciNetCrossRef
10.
Diba, A., Sharma, V., Pazandeh, A., Pirsiavash, H., Van Gool, L.: Weakly supervised cascaded convolutional networks. In: IEEE CVPR (2017)
11.
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. IJCV 111(1), 98–136 (2014)CrossRef
12.
Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware CNN model. In: IEEE ICCV, pp. 1134–1142 (2015)
13.
Girshick, R.: Fast R-CNN. In: IEEE ICCV, pp. 1440–1448 (2015)
14.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE CVPR, pp. 580–587 (2014)
15.
16.
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM Multimedia, pp. 675–678 (2014)
17.
Jie, Z., Wei, Y., Jin, X., Feng, J., Liu, W.: Deep self-taught learning for weakly supervised object localization. In: IEEE CVPR (2017)
18.
19.
Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In: ECCV, pp. 695–711 (2016)
20.
Lai, B., Gong, X.: Saliency guided end-to-end learning for weakly supervised object detection. In: IJCAI (2017)
21.
Li, D., Huang, J.B., Li, Y., Wang, S., Yang, M.H.: Weakly supervised object localization with progressive domain adaptation. In: IEEE CVPR, pp. 3512–3520 (2016)
22.
Li, J., et al.: Attentive contexts for object detection. IEEE Trans. Multimedia 19(5), 944–954 (2017)CrossRef
23.
Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., Yan, S.: Towards computational baby learning: a weakly-supervised approach for object detection. In: IEEE ICCV, pp. 999–1007 (2015)
24.
Lin, M., Chen, Q., Yan, S.: Network in network. In: ICLR (2013)
25.
26.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE CVPR (2015)
27.
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: IEEE CVPR, pp. 685–694 (2015)
28.
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: IEEE CVPR (2017)
29.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)
30.
Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., Xue, X.: DSOD: learning deeply supervised object detectors from scratch. In: IEEE ICCV (2017)
31.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
32.
Tang, P., Wang, X., Bai, X., Liu, W.: Multiple instance detection network with online instance classifier refinement. In: IEEE CVPR (2017)
33.
Teh, E.W., Rochan, M., Wang, Y.: Attention networks for weakly supervised object localization. In: BMVC (2016)
34.
Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. IJCV 104(2), 154–171 (2013)CrossRef
35.
36.
Wei, Y., Feng, J., Liang, X., Cheng, M.M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: IEEE CVPR (2017)
37.
Wei, Y., et al.: Learning to segment with image-level annotations. Pattern Recogn. (2016)
38.
Wei, Y., et al.: STC: a simple to complex framework for weakly-supervised semantic segmentation. IEEE TPAMI (2016)
39.
Wei, Y., et al.: HCP: a flexible cnn framework for multi-label image classification. IEEE TPAMI 38(9), 1901–1907 (2016)CrossRef
40.
Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: a simple approach for weakly-and semi-supervised semantic segmentation. In: IEEE CVPR, pp. 7268–7277
41.
Xiao, H., Feng, J., Wei, Y., Zhang, M., Yan, S.: Deep salient object detection with dense connections and distraction diagnosis. IEEE Trans. Multimedia (2018)
42.
Zhang, X., Wei, Y., Feng, J., Yang, Y., Huang, T.: Adversarial complementary learning for weakly supervised object localization. In: IEEE CVPR (2018)
43.
Zhang, X., Wei, Y., Kang, G., Yang, Y., Huang, T.: Self-produced guidance for weakly-supervised object localization. In: ECCV (2018)
44.
Zhao, F., Li, J., Zhao, J., Feng, J.: Weakly supervised phrase localization with multi-scale anchored transformer network. In: IEEE CVPR, pp. 5696–5705 (2018)
45.
Zhou, B., Khosla, A.A.L., Oliva, A., Torralba, A.: Learning Deep Features for Discriminative Localization. IEEE CVPR (2016)
46.
Zhu, Y., Urtasun, R., Salakhutdinov, R., Fidler, S.: segDeepM: exploiting segmentation and context in deep neural networks for object detection. In: IEEE CVPR, pp. 4703–4711 (2015)
47.
Metadaten
Titel
TSC: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
verfasst von
Yunchao Wei
Zhiqiang Shen
Bowen Cheng
Honghui Shi
Jinjun Xiong
Jiashi Feng
Thomas Huang
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01251-9

Electronic ISBN: 978-3-030-01252-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01252-6_27

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