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Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation Read chapter Read first chapter

2020 | OriginalPaper | Chapter

LabelEnc: A New Intermediate Supervision Method for Object Detection

Authors : Miao Hao, Yitao Liu, Xiangyu Zhang, Jian Sun

Published in: Computer Vision – ECCV 2020

Publisher: Springer International Publishing

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Abstract

In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems. The key idea is to introduce a novel label encoding function, mapping the ground-truth labels into latent embedding, acting as an auxiliary intermediate supervision to the detection backbone during training. Our approach mainly involves a two-step training procedure. First, we optimize the label encoding function via an AutoEncoder defined in the label space, approximating the “desired” intermediate representations for the target object detector. Second, taking advantage of the learned label encoding function, we introduce a new auxiliary loss attached to the detection backbones, thus benefiting the performance of the derived detector. Experiments show our method improves a variety of detection systems by around 2% on COCO dataset, no matter one-stage or two-stage frameworks. Moreover, the auxiliary structures only exist during training, i.e. it is completely cost-free in inference time.

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Literature
1.
Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for attribute-based classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 819–826 (2013)
2.
3.
Bengio, S., Weston, J., Grangier, D.: Label embedding trees for large multi-class tasks. In: Advances in Neural Information Processing Systems, pp. 163–171 (2010)
4.
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
5.
6.
Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. In: Advances in Neural Information Processing Systems, pp. 742–751 (2017)
7.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:​2002.​05709 (2020)
8.
Chen, Y., Wang, N., Zhang, Z.: DarkRank: accelerating deep metric learning via cross sample similarities transfer. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
9.
Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)
10.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
11.
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
12.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:​1911.​05722 (2019)
13.
He, K., Girshick, R., Dollár, P.: Rethinking ImageNet pre-training. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4918–4927 (2019)
14.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
15.
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)CrossRef
16.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
17.
18.
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:​1502.​03167 (2015)
19.
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562–570 (2015)
20.
Lee, W., Na, J., Kim, G.: Multi-task self-supervised object detection via recycling of bounding box annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4984–4993 (2019)
21.
Li, Q., Jin, S., Yan, J.: Mimicking very efficient network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6356–6364 (2017)
22.
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: DetNet: a backbone network for object detection. arXiv preprint arXiv:​1804.​06215 (2018)
23.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
24.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
25.
26.
27.
Luo, P., Zhu, Z., Liu, Z., Wang, X., Tang, X.: Face model compression by distilling knowledge from neurons. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
28.
Mostajabi, M., Maire, M., Shakhnarovich, G.: Regularizing deep networks by modeling and predicting label structure. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5629–5638 (2018)
29.
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)
30.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
31.
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
32.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
33.
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. arXiv preprint arXiv:​1412.​6550 (2014)
34.
Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., Xue, X.: DSOD: learning deeply supervised object detectors from scratch. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1919–1927 (2017)
35.
Sun, X., Wei, B., Ren, X., Ma, S.: Label embedding network: learning label representation for soft training of deep networks. arXiv preprint arXiv:​1710.​10393 (2017)
36.
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
37.
Tang, Z., Wang, D., Zhang, Z.: Recurrent neural network training with dark knowledge transfer. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5900–5904. IEEE (2016)
38.
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9627–9636 (2019)
39.
Wang, T., Yuan, L., Zhang, X., Feng, J.: Distilling object detectors with fine-grained feature imitation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4933–4942 (2019)
40.
Wang, X., Zhang, R., Sun, Y., Qi, J.: KDGAN: knowledge distillation with generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 775–786 (2018)
41.
42.
43.
Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:​1612.​03928 (2016)
44.
45.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
46.
Zhu, R., et al.: ScratchDet: training single-shot object detectors from scratch. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2268–2277 (2019)
47.
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9308–9316 (2019)
Metadata
Title
LabelEnc: A New Intermediate Supervision Method for Object Detection
Authors
Miao Hao
Yitao Liu
Xiangyu Zhang
Jian Sun
Copyright Year
2020
Book
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58594-5

Electronic ISBN: 978-3-030-58595-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58595-2_32

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