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The detection of oriented objects is frequently appeared in the field of natural scene text detection as well as object detection in aerial images.
Support-vector networks
Cortes, C., Vapnik, V.: · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: · 1998
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
Earlier work this paper cites.
Fast r-cnn
Girshick, R.: · 2015
Earlier work this paper cites.
Icdar 2015 competition on robust reading
Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., Lu, S., et al.: · 2015
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Earlier work this paper cites.
Stacked hourglass networks for human pose estimation
Newell, A., Yang, K., Deng, J.: · 2016
Earlier work this paper cites.
Detecting text in natural image with connectionist text proposal network
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: · 2016
Cited alongside, same era.
R2cnn: Rotational region cnn for orientation robust scene text detection
Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., Luo, Z.: · 2017
Cited alongside, same era.
Yolo9000: better, faster, stronger
Redmon, J., Farhadi, A.: · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: · 2017
Cited alongside, same era.
Detecting oriented text in natural images by linking segments
Shi, B., Bai, X., Belongie, S.: · 2017
Dota: A large-scale dataset for object detection in aerial images
Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L.: · 2018
Later among the works it cites.
Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks
Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., Guo, Z.: · 2018
Later among the works it cites.
Towards multi-class object detection in unconstrained remote sensing imagery
Azimi, S.M., Vig, E., Bahmanyar, R., Körner, M., Reinartz, P.: · 2018
Later among the works it cites.
Fots: Fast oriented text spotting with a unified network
Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J.: · 2018
Later among the works it cites.
An analysis of scale invariance in object detection snip
Singh, B., Davis, L.S.: · 2018
Later among the works it cites.
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Cited alongside, same era.
East: An efficient and accurate scene text detector
Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2017
Cited alongside, same era.
Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: · 2017
Cited alongside, same era.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
Cited alongside, same era.
Arbitrary-oriented scene text detection via rotation proposals
Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., Xue, X.: · 2018
Cited alongside, same era.
Cornernet: Detecting objects as paired keypoints
Law, H., Deng, J.: · 2018
Cited alongside, same era.
Path aggregation network for instance segmentation
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: · 2018
Later among the works it cites.
Single-shot refinement neural network for object detection
Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: · 2018
Later among the works it cites.
Scrdet: Towards more robust detection for small, cluttered and rotated objects
Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., Fu, K.: · 2019
Closest in time.
Bottom-up object detection by grouping extreme and center points
Zhou, X., Zhuo, J., Krahenbuhl, P.: · 2019
Closest in time.
Fcos: Fully convolutional one-stage object detection
Tian, Z., Shen, C., Chen, H., He, T.: · 2019
Closest in time.
Zhou, X., Wang, D., Krähenbühl, P.: · 2019
Closest in time.
Learning roi transformer for detecting oriented objects in aerial images
Jian Ding, Nan Xue, Y.L.G.S.X.Q.L.: · 2019
Closest in time.
R3det: Refined single-stage detector with feature refinement for rotating object
Yang, X., Liu, Q., Yan, J., Li, A.: · 2019
Closest in time.