1. bookVolumen 13 (2023): Edición 1 (January 2023)
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eISSN
2449-6499
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30 Dec 2014
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Training CNN Classifiers Solely on Webly Data

Publicado en línea: 28 Nov 2022
Volumen & Edición: Volumen 13 (2023) - Edición 1 (January 2023)
Páginas: 75 - 92
Recibido: 24 Mar 2022
Aceptado: 19 Oct 2022
Detalles de la revista
License
Formato
Revista
eISSN
2449-6499
Primera edición
30 Dec 2014
Calendario de la edición
4 veces al año
Idiomas
Inglés

[1] J. A. Aghamaleki and S. M. Baharlou. Transfer learning approach for classification and noise reduction on noisy web data. Expert Syst. Appl., 105:221–232, 2018.10.1016/j.eswa.2018.03.042 Search in Google Scholar

[2] Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. Label-embedding for attribute-based classification. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013, pages 819–826. IEEE Computer Society, 2013.10.1109/CVPR.2013.111 Search in Google Scholar

[3] S. Bai and S. An. A survey on automatic image caption generation. Neurocomputing, 311:291–304, 2018.10.1016/j.neucom.2018.05.080 Search in Google Scholar

[4] A. Bergamo and L. Torresani. Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. In J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada, pages 181–189. Curran Associates, Inc., 2010. Search in Google Scholar

[5] J. Böhlke, D. Korsch, P. Bodesheim, and J. Denzler. Lightweight filtering of noisy web data: Augmenting fine-grained datasets with selected internet images. In G. M. Farinella, P. Radeva, J. Braz, and K. Bouatouch, editors, Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, Volume 5: VISAPP, Online Streaming, February 8-10, 2021, pages 466–477. SCITEPRESS, 2021. Search in Google Scholar

[6] L. Bossard, M. Guillaumin, and L. Van Gool. Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision, 2014.10.1007/978-3-319-10599-4_29 Search in Google Scholar

[7] X. Chen and A. Gupta. Webly supervised learning of convolutional networks. In 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015, pages 1431–1439. IEEE Computer Society, 2015.10.1109/ICCV.2015.168 Search in Google Scholar

[8] A. Dosovitskiy, P. Fischer, E. Ilg, P. Häausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015, pages 2758–2766. IEEE Computer Society, 2015.10.1109/ICCV.2015.316 Search in Google Scholar

[9] T. Durand, N. Thome, and M. Cord. WELDON: weakly supervised learning of deep convolutional neural networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 4743–4752. IEEE Computer Society, 2016.10.1109/CVPR.2016.513 Search in Google Scholar

[10] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338, June 2010.10.1007/s11263-009-0275-4 Search in Google Scholar

[11] A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov. Devise: A deep visual-semantic embedding model. In C. J. C. Burges, L. Bottou, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pages 2121–2129, 2013. Search in Google Scholar

[12] R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 580–587. IEEE Computer Society, 2014.10.1109/CVPR.2014.81 Search in Google Scholar

[13] R. Gomez. Instacities1m, https://gombru.github.io/2018/08/01/InstaCities1M/, 2018. Search in Google Scholar

[14] R. Gomez, L. Gómez, J. Gibert, and D. Karatzas. Learning to learn from web data through deep semantic embeddings. In L. Leal-Taixé and S. Roth, editors, Computer Vision - ECCV 2018 Workshops - Munich, Germany, September 8-14, 2018, Proceedings, Part VI, volume 11134 of Lecture Notes in Computer Science, pages 514–529. Springer, 2018.10.1007/978-3-030-11024-6_40 Search in Google Scholar

[15] A. Gupta, A. Vedaldi, and A. Zisserman. Synthetic data for text localisation in natural images. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 2315–2324. IEEE Computer Society, 2016.10.1109/CVPR.2016.254 Search in Google Scholar

[16] K. He, G. Gkioxari, P. Dollár, and R. B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pages 2980–2988. IEEE Computer Society, 2017.10.1109/ICCV.2017.322 Search in Google Scholar

[17] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 770–778. IEEE Computer Society, 2016.10.1109/CVPR.2016.90 Search in Google Scholar

[18] D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. In S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 10477–10486, 2018. Search in Google Scholar

[19] H. Izadinia, B. C. Russell, A. Farhadi, M. D. Hoffman, and A. Hertzmann. Deep classifiers from image tags in the wild. In G. Friedland, C. Ngo, and D. A. Shamma, editors, Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions, MM-Commons 2015, Brisbane, Australia, October 30, 2015, pages 13–18. ACM, 2015.10.1145/2814815.2814821 Search in Google Scholar

[20] M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman. Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis., 116(1):1–20, 2016.10.1007/s11263-015-0823-z Search in Google Scholar

[21] A. Joulin, L. van der Maaten, A. Jabri, and N. Vasilache. Learning visual features from large weakly supervised data. In B. Leibe, J. Matas, N. Sebe, and M. Welling, editors, Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VII, volume 9911 of Lecture Notes in Computer Science, pages 67–84. Springer, 2016.10.1007/978-3-319-46478-7_5 Search in Google Scholar

[22] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In Y. Bengio and Y. LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. Search in Google Scholar

[23] J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei. The unreasonable effectiveness of noisy data for fine-grained recognition. In B. Leibe, J. Matas, N. Sebe, and M. Welling, editors, Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III, volume 9907 of Lecture Notes in Computer Science, pages 301–320. Springer, 2016.10.1007/978-3-319-46487-9_19 Search in Google Scholar

[24] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In P. L. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, pages 1106–1114, 2012. Search in Google Scholar

[25] D. Lewy and J. Mańdziuk. An overview of mixing augmentation methods and augmentation strategies. Artificial Intelligence Review, 2022.10.1007/s10462-022-10227-z Search in Google Scholar

[26] D. Lewy and J. Mańdziuk. Instafood1m, https://szefkuchni.github.io/InstaFood1M/, 2019. Search in Google Scholar

[27] D. Lewy and J. Mańdziuk. Instapascal2m, https://szefkuchni.github.io/InstaPascal2M/, 2019. Search in Google Scholar

[28] J. Li, Y. Song, J. Zhu, L. Cheng, Y. Su, L. Ye, P. Yuan, and S. Han. Learning from large-scale noisy web data with ubiquitous reweighting for image classification. IEEE Trans. Pattern Anal. Mach. Intell., 43(5):1808–1814, 2021.10.1109/TPAMI.2019.296191031880542 Search in Google Scholar

[29] T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: common objects in context. In D. J. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, editors, Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V, volume 8693 of Lecture Notes in Computer Science, pages 740–755. Springer, 2014.10.1007/978-3-319-10602-1_48 Search in Google Scholar

[30] D. Mahajan, R. B. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten. Exploring the limits of weakly supervised pretraining. In V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, editors, Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part II, volume 11206 of Lecture Notes in Computer Science, pages 185–201. Springer, 2018.10.1007/978-3-030-01216-8_12 Search in Google Scholar

[31] L. Niu, W. Li, D. Xu, and J. Cai. Visual recognition by learning from web data via weakly supervised domain generalization. IEEE Trans. Neural Networks Learn. Syst., 28(9):1985–1999, 2017.10.1109/TNNLS.2016.255734927254873 Search in Google Scholar

[32] L. Niu, Q. Tang, A. Veeraraghavan, and A. Sabharwal. Learning from noisy web data with category-level supervision. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 7689–7698. Computer Vision Foundation / IEEE Computer Society, 2018.10.1109/CVPR.2018.00802 Search in Google Scholar

[33] L. Niu, A. Veeraraghavan, and A. Sabharwal. Webly supervised learning meets zero-shot learning: A hybrid approach for fine-grained classification. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 7171–7180. Computer Vision Foundation / IEEE Computer Society, 2018.10.1109/CVPR.2018.00749 Search in Google Scholar

[34] G. Patrini, A. Rozza, A. K. Menon, R. Nock, and L. Qu. Making deep neural networks robust to label noise: A loss correction approach. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2233–2241. IEEE Computer Society, 2017.10.1109/CVPR.2017.240 Search in Google Scholar

[35] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever. Learning transferable visual models from natural language supervision. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8748–8763. PMLR, 2021. Search in Google Scholar

[36] J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 779–788. IEEE Computer Society, 2016.10.1109/CVPR.2016.91 Search in Google Scholar

[37] S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: towards real-time object detection with region proposal networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 91–99, 2015. Search in Google Scholar

[38] M. T. Ribeiro, S. Singh, and C. Guestrin. “why should I trust you?”: Explaining the predictions of any classifier. In B. Krishnapuram, M. Shah, A. J. Smola, C. C. Aggarwal, D. Shen, and R. Rastogi, editors, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pages 1135–1144. ACM, 2016.10.1145/2939672.2939778 Search in Google Scholar

[39] S. Ruder. An overview of gradient descent optimization algorithms. CoRR, abs/1609.04747, 2016. Search in Google Scholar

[40] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis., 115(3):211–252, 2015.10.1007/s11263-015-0816-y Search in Google Scholar

[41] E. Shelhamer, J. Long, and T. Darrell. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 39(4):640–651, 2017.10.1109/TPAMI.2016.257268327244717 Search in Google Scholar

[42] A. Shrivastava, A. Gupta, and R. B. Girshick. Training region-based object detectors with online hard example mining. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 761–769. IEEE Computer Society, 2016.10.1109/CVPR.2016.89 Search in Google Scholar

[43] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Y. Bengio and Y. LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. Search in Google Scholar

[44] E. Strubell, A. Ganesh, and A. McCallum. Energy and policy considerations for deep learning in NLP. In A. Korhonen, D. R. Traum, and L. Màrquez, editors, Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 3645–3650. Association for Computational Linguistics, 2019.10.18653/v1/P19-1355 Search in Google Scholar

[45] H. Su, S. Gong, and X. Zhu. Weblogo-2m: Scalable logo detection by deep learning from the web. In 2017 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2017, Venice, Italy, October 22-29, 2017, pages 270–279. IEEE Computer Society, 2017.10.1109/ICCVW.2017.41 Search in Google Scholar

[46] C. Sun, A. Shrivastava, S. Singh, and A. Gupta. Revisiting unreasonable effectiveness of data in deep learning era. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pages 843–852. IEEE Computer Society, 2017.10.1109/ICCV.2017.97 Search in Google Scholar

[47] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pages 1–9. IEEE Computer Society, 2015.10.1109/CVPR.2015.7298594 Search in Google Scholar

[48] A. Vahdat. Toward robustness against label noise in training deep discriminative neural networks. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5596–5605, 2017. Search in Google Scholar

[49] A. Veit, N. Alldrin, G. Chechik, I. Krasin, A. Gupta, and S. J. Belongie. Learning from noisy large-scale datasets with minimal supervision. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 6575–6583. IEEE Computer Society, 2017.10.1109/CVPR.2017.696 Search in Google Scholar

[50] Y. Xian, B. Schiele, and Z. Akata. Zero-shot learning - the good, the bad and the ugly. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 3077–3086. IEEE Computer Society, 2017.10.1109/CVPR.2017.328 Search in Google Scholar

[51] T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang. Learning from massive noisy labeled data for image classification. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pages 2691–2699. IEEE Computer Society, 2015. Search in Google Scholar

[52] J. Yang, X. Sun, Y. Lai, L. Zheng, and M. Cheng. Recognition from web data: A progressive filtering approach. IEEE Trans. Image Process., 27(11):5303–5315, 2018. Search in Google Scholar

[53] I. Yildirim, T. Kulkarni, W. A. Freiwald, and J. B. Tenenbaum. Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. In Annual Conference of the Cognitive Science Society, 2015. Search in Google Scholar

[54] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pages 3320–3328, 2014. Search in Google Scholar

[55] C. Zhang, Y. Yao, X. Xu, J. Shao, J. Song, Z. Li, and Z. Tang. Extracting useful knowledge from noisy web images via data purification for fine-grained recognition. In H. T. Shen, Y. Zhuang, J. R. Smith, Y. Yang, P. Cesar, F. Metze, and B. Prabhakaran, editors, MM ’21: ACM Multimedia Conference, Virtual Event, China, October 20 - 24, 2021, pages 4063–4072. ACM, 2021. Search in Google Scholar

[56] B. Zhuang, L. Liu, Y. Li, C. Shen, and I. D. Reid. Attend in groups: A weakly-supervised deep learning framework for learning from web data. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2915–2924. IEEE Computer Society, 2017.10.1109/CVPR.2017.311 Search in Google Scholar

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