Abstract
Training of a convolutional neural network for object detection requires a large number of images with pixel-level annotations. Weakly supervised learning uses image-level labels to circumvent the issue of lack of semantic examples, which remains an open challenging. This paper proposes a cascaded deep network architecture that leverages the class activation mapping with global average pooling. The first stage of this architecture learns to infer object localization maps based on the image-level annotations, which generates bounding boxes of objects in every image. These image patches are adhesion areas in the original image. In the second stage, the image patches are used to train the detection network. Experiments are conducted using the PASCAL VOC 2012 datasets. Our proposed method obtains a mean average precision of 87.2% and demonstrates a competitive performance of classification performance with respect to the state-of-the-art methods. In the evaluation of object localization, the recall of our method is improved by 9%.
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References
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization. in Computer Vision and Pattern Recognition, 2016
G. Papandreou, L.C. Chen, K. Murphy, A.L. Yuille, Weakly-and semi-supervised learning of a CNN for semantic image segmentation. in International Conference on Computer Vision, 2015
A. Chaudhry, P.K. Dokania, P.H.S. Torr, Discovering class-specific pixels for weakly supervised semantic segmentation. in Computer Vision and Pattern Recognition, 2017
N. Liu, J. Han, DHSNet: deep hierarchical saliency network for salient object detection. in IEEE Conference on Computer Vision and Pattern Recognition, pp. 678–686, 2016
A. Kolesnikov, C.H. Lampert, Seed expand and constrain: three principles for weakly-supervised. In European Conference on Computer Vision, pp. 695–711, 2016
H. Zhang, Z. Kyaw, J. Yu, S.F. Chang, PPR-FCN: weakly supervised visual relation detection via parallel pairwise R-FCN. in IEEE International Conference on Computer Vision, pp. 4243–4251, 2017
K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition. In European Conference on Computer Vision, pp. 346–361, 2014
M. Tang, A. Djelouah, F. Perazzi, Y. Boykov, C. Schroers, Normalized cut loss for weakly supervised CNN segment. in Computer Vision and Pattern Recognition, 2018
J. Long, E. Shelhamer, T. Darrell. Fully convolutional networks for semantic segmentation. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440, 2015
J. Dai, K. He, J. Sun, BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. in IEEE International Conference on Computer Vision, pp. 1635–1643, 2015
P.O. Pinheiro, R. Collobert. Weakly supervised semantic segmentation with convolutional networks. in Computer Vision and Pattern Recognition, 2015
D. Lin, J. Dai, J. Jia, K. He, J. Sun, ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3159–3167, 2016
N. Krishnaraj, M. Elhoseny, M. Thenmozhi, M.M. Selim, K. Shankar, Deep learning model for real-time image compression in Internet of Underwater Things (IoUT). J. Real-Time Image Process. 2019. https://doi.org/10.1007/s11554-019-00879-6
M. Elhoseny, G.-B. Bian, S.K. Lakshmanaprabu, K. Shankar, A.K. Singh, W. Wu, Effective features to classify ovarian cancer data in internet of medical things. Comput. Netw. 159, 147–156 (2019)
B.S. Murugan, M. Elhoseny, K. Shankar, J. Uthayakumar, Region-based scalable smart system for anomaly detection in pedestrian walkways. Comput. Electr. Eng. 75, 146–160 (2019)
K. Shankar, M. Elhoseny, S.K. Lakshmanaprabu, M. Ilayaraja, R.M. Vidhyavathi, M. Alkhambashi, Optimal feature level fusion based ANFIS classifier for brain MRI image classification. Concurrency Comput. Pract. Exp. 2018. https://doi.org/10.1002/cpe.4887
X. Yuan, D. Li, D. Mohapatra, M. Elhoseny, Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding. Comput. Electr. Eng. 70, 813–825 (2018)
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, OverFeat: integrated recognition, localization and detection using convolutional networks. in IEEE Conference on Computer Vision and Pattern Recognition, 2013
T. Durand, T. Mordan, N. Thome, M. Cord, WILDCAT: weakly supervised learning of deep ConvNets for image classification, pointwise localization, and segmentation. in IEEE Conference on Computer Vision and Pattern Recognition, pp. 5957–5966, 2017
Y. Wei, X. Liang, Y. Chen, X. Shen, M.-M. Cheng, J. Feng, Y. Zhao, S. Yan, STC: a simple to complex framework for weakly supervised semantic segmentation. in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
Y. Wei, J. Feng, X. Liang, M.M. Cheng, Y. Zhao, S. Yan, Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. in Conference on Computer Vision and Pattern Recognition, 2017
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Shi, J., Yuan, X., Elhoseny, M., Yuan, X. (2020). Weakly Supervised Deep Learning for Objects Detection from Images. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_18
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DOI: https://doi.org/10.1007/978-3-030-45099-1_18
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