Fetching the paper…
Reading the bibliography…
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image.
1901
Earlier work this paper cites.
2007
Earlier work this paper cites.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 580–587 (2014)
2014
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: European Conference on Computer Vision. pp. 346–361. Springer (2014)
2014
Earlier work this paper cites.
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1440–1448 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Jaderberg, M., Simonyan, K., Zisserman, A., kavukcuoglu, k.: Spatial transformer networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28, pp. 2017–2025. Curran Associates, Inc. (2015), http://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf
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.
Gidaris, S., Komodakis, N.: Attend refine repeat: Active box proposal generation via in-out localization. In: Richard C. Wilson, E.R.H., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference (BMVC). pp. 90.1–90.13. BMVA Press (September 2016). https://doi.org/10.5244/C.30.90, https://dx.doi.org/10.5244/C.30.90
2016
Earlier work this paper cites.
Gidaris, S., Komodakis, N.: Locnet: Improving localization accuracy for object detection. In: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on (2016)
2016
Earlier work this paper cites.
Li, Y., He, K., Sun, J., et al.: 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.
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.
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)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Later among the works it cites.
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)
2018
Later among the works it cites.
Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: The European Conference on Computer Vision (ECCV) (September 2018)
2018
Later among the works it cites.
Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: The IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask r-cnn. In: The IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Cited alongside, same era.
Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR. vol. 1, p. 4 (2017)
2017
Cited alongside, same era.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 2999–3007. IEEE (2017)
2017
Cited alongside, same era.
Redmon, J., Farhadi, A.: Yolo9000: Better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6517–6525. IEEE (2017)
2017
Cited alongside, same era.
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 6569–6578 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: Reppoints: Point set representation for object detection. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.