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Faster r-cnn: Towards real-time object detection with region proposal networks. Alban Desmaison: Luca Antiga, and Adam Lerer. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., & DeVito, Z. Convolutional stn for weakly supervised object localization and beyond. Meethal, A., Pedersoli, M., Belharbi, S., & Granger, E. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. Erasing integrated learning: A simple yet effective approach for weakly supervised object localization. In 2020 IEEE international conference on multimedia and expo (ICME) (pp. Double shot: Preserve and erase based class attention networks for weakly supervised localization (peca-net). Luo, L., Yuan, C., Zhang, K., Jiang, Y., Zhang, Y., & Zhang, H. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. Fully convolutional networks for semantic segmentation. In European conference on computer vision (pp. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. In Proceedings of the IEEE international conference on computer vision, pp. Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. Feature pyramid networks for object detection. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S.
In Advances in neural information processing systems (pp. Jaderberg, M., Simonyan, K., & Zisserman, A., et al. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Deep residual learning for image recognition. He, K., Gkioxari, G., Dollár, P., & Girshick, R. Real time image saliency for black box classifiers. Autoaugment: Learning augmentation strategies from data. Attention-based dropout layer for weakly supervised object localization. Also, we confirmed through experiments that our proposed method outperforms state-of-the-art methods on the CUB-200-2011 and ILSVRC datasets.Ĭhoe, J., & Shim, H. In particular, the second advantage alleviates a significant burden such as hyperparameter tuning. Second, we don’t have to require solving complex optimization problem. First, our proposed model improves localization. Therefore, we can use high-quality and abundant information for localization, which induces several advantages. In our proposed model, FPN produces multi-scale and high-quality feature maps, and then these feature maps are gathered to conduct classification. To be more efficient WSOL, we propose a new architecture that utilizes feature pyramid network (FPN) and multi-scale information to deal with simplified optimization and to improve the localization. Further, they introduce more complex optimization problems than the classification problem to compensate for the lack of resources such as bounding box annotation. However, most WSOL methods tend to find a specific part of an object. The purpose of weakly supervised object localization (WSOL) is to localize an object requiring only classification labels.
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