Fetching the paper…
Reading the bibliography…
In recent years, object detection has experienced impressive progress.
Bottema, M.J., Slavotinek, J.P.: Detection and classification of lobular and dcis (small cell) microcalcifications in digital mammograms. Pattern Recognition Letters 21
2000
Earlier work this paper cites.
Abouelela, A., Abbas, H.M., Eldeeb, H., Wahdan, A.A., Nassar, S.M.: Automated vision system for localizing structural defects in textile fabrics. Pattern recognition letters 26
2005
Earlier work this paper cites.
Ng, H.F.: Automatic thresholding for defect detection. Pattern recognition letters 27
2006
Earlier work this paper cites.
Modegi, T.: Small object recognition techniques based on structured template matching for high-resolution satellite images. In: SICE Annual Conference, 2008. pp. 2168–2173. IEEE (2008)
2008
Earlier work this paper cites.
Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: Neural Networks (IJCNN), The 2011 International Joint Conference on. pp. 2809–2813. IEEE (2011)
2011
Earlier work this paper cites.
Deshmukh, V.R., Patnaik, G., Patil, M.: Real-time traffic sign recognition system based on colour image segmentation. International Journal of Computer Applications 83
2013
Earlier work this paper cites.
Nagarajan, M.B., Huber, M.B., Schlossbauer, T., Leinsinger, G., Krol, A., Wismüller, A.: Classification of small lesions in dynamic breast mri: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement. Machine vision and applications 24
2013
Earlier work this paper cites.
Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2056–2063 (2013)
2013
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., Urtasun, R.: 3d object proposals for accurate object class detection. In: Advances in Neural Information Processing Systems. pp. 424–432 (2015)
2015
Earlier work this paper cites.
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)
2015
Earlier work this paper cites.
Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R.: Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2874–2883 (2016)
2016
Earlier work this paper cites.
Chen, C., Liu, M.Y., Tuzel, O., Xiao, J.: R-cnn for small object detection. In: Asian conference on computer vision. pp. 214–230. Springer (2016)
2016
Cited alongside, same era.
Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3150–3158 (2016)
2016
Cited alongside, same era.
Dai, J., Li, Y., He, K., Sun, J.: R-fcn: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems. pp. 379–387 (2016)
2016
Cited alongside, same era.
Kampffmeyer, M., Salberg, A.B., Jenssen, R.: Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 1–9 (2016)
2016
Cited alongside, same era.
Hu, P., Ramanan, D.: Finding tiny faces. In: Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. pp. 1522–1530. IEEE (2017)
2017
Later among the works it cites.
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: IEEE CVPR. vol. 4 (2017)
2017
Later among the works it cites.
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: IEEE CVPR (2017)
2017
Later among the works it cites.
Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. pp. 4438–4446 (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision. pp. 21–37. Springer (2016)
2016
Cited alongside, same era.
Yang, F., Choi, W., Lin, Y.: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2129–2137 (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Eggert, C., Zecha, D., Brehm, S., Lienhart, R.: Improving small object proposals for company logo detection. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. pp. 167–174. ACM (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Computer Vision (ICCV), 2017 IEEE International Conference on. pp. 2980–2988. IEEE (2017)
2017
Cited alongside, same era.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR. vol. 1, p. 4 (2017)
2017
Later among the works it cites.
Menikdiwela, M., Nguyen, C., Li, H., Shaw, M.: Cnn-based small object detection and visualization with feature activation mapping. In: 2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017, Christchurch, New Zealand, December 4-6, 2017. pp. 1–5 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Cao, G., Xie, X., Yang, W., Liao, Q., Shi, G., Wu, J.: Feature-fused ssd: fast detection for small objects. In: Ninth International Conference on Graphic and Image Processing (ICGIP 2017). vol. 10615, p. 106151E. International Society for Optics and Photonics (2018)
2018
Later among the works it cites.
Cheng, P., Liu, W., Zhang, Y., Ma, H.: Loco: Local context based faster r-cnn for small traffic sign detection. In: International Conference on Multimedia Modeling. pp. 329–341. Springer (2018)
2018
Later among the works it cites.
Fang, L., Zhao, X., Zhang, S.: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications pp. 1–19 (2018)
2018
Later among the works it cites.
Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: Detectron. https://github.com/facebookresearch/detectron (2018)
2018
Later among the works it cites.
Ren, Y., Zhu, C., Xiao, S.: Small object detection in optical remote sensing images via modified faster r-cnn. Applied Sciences 8
2018
Later among the works it cites.