Chosen Methods of Improving Small Object Recognition with Weak Recognizable Features

1. Ren, S., Ross, K.H., Sun, G.J.: Faster R-CNN: towards real-time object detection with region proposal networks. https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf

2. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

3. Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection (2017). https://arxiv.org/pdf/1706.05274.pdf

4. Everingham, M., van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL visual object classes homepage (2014). http://host.robots.ox.ac.uk/pascal/VOC/

5. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014). https://arxiv.org/pdf/1311.2524.pdf

6. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2016). https://arxiv.org/pdf/1506.02640.pdf

7. Liu, W., et al.: SSD: single shot multibox detector (2016). https://arxiv.org/pdf/1512.02325.pdf

8. Ledig, C., et al.: Twitter. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2017). https://arxiv.org/pdf/1609.04802.pdf

9. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2016). https://arxiv.org/pdf/1511.06434.pdf

10. Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10, Cifar-100 dataset (2009). https://www.cs.toronto.edu/kriz/cifar.html

11. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015). https://arxiv.org/pdf/1409.1556.pdf

12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). https://arxiv.org/pdf/1512.03385.pdf

13. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification (2018). https://arxiv.org/pdf/1803.01229.pdf

14. Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks (2019). https://arxiv.org/pdf/1801.02610.pdf

15. Lin, T.-Y., et al.: Microsoft COCO: common objects in context (2015). https://arxiv.org/pdf/1405.0312.pdf

16. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection (2018). https://arxiv.org/pdf/1708.02002.pdf

17. Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection (2017). https://arxiv.org/pdf/1612.03144.pdf

18. Hu, P., Ramanan, D.: Finding tiny faces (2017). https://arxiv.org/pdf/1612.04402.pdf

19. Lim, J.-S., Astrid, M., Yoon, H.-J., Lee, S.-I.: Small object detection using context and attention (2019). https://arxiv.org/pdf/1912.06319.pdf

20. Hu, G., Yang, Z., Hu, L., Huang, L., Han, J.: Small object detection with multiscale features. Int. J. Digit. Multimedia Broadcast. 1–10 (2018)

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21. Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., Cho, K.: Augmentation for small object detection (2019). https://arxiv.org/pdf/1902.07296.pdf

22. Chen, Y., Li, J., Niu, Y., He, J.: Small object detection networks based on classification-oriented super-resolution GAN for UAV aerial imagery. In: Chinese Control And Decision Conference (CCDC), pp. 4610–4615 (2019)

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23. Jiang, W., Ying, N.: Improve object detection by data enhancement based on generative adversarial nets. https://arxiv.org/pdf/1903.01716.pdf

24. Bai, Y., Zhang, Y., Ding, M., Ghanem, B.: SOD-MTGAN: small object detection via multi-task generative adversarial network (2018)

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25. Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia (2013)

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26. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples, arXiv preprint arXiv:1412.6572 (2014)

27. Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)

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28. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2016)

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