Fetching the paper…
Reading the bibliography…
The goal of object detection is to determine the class and location of objects in an image.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88
2010
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)
2012
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.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Computer science (2014)
2014
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 (2014)
2014
Earlier work this paper cites.
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.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521
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.
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
Earlier work this paper cites.
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)
2016
Earlier work this paper cites.
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 (2016)
2016
Earlier work this paper cites.
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European conference on computer vision. pp. 483–499 (2016)
2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: An advanced object detection network. In: Proceedings of the 24th ACM international conference on Multimedia. pp. 516–520 (2016)
2016
Earlier work this paper cites.
Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-nms–improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision. pp. 5561–5569 (2017)
2017
Earlier work this paper cites.
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 764–773 (2017)
2017
Cited alongside, same era.
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)
2017
Cited alongside, same era.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (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: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117–2125 (2017)
2017
Cited alongside, same era.
Li, Y., Chen, Y., Wang, N., Zhang, Z.: Scale-aware trident networks for object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 6054–6063 (2019)
2019
Later among the works it cites.
Lu, X., Li, B., Yue, Y., Li, Q., Yan, J.: Grid r-cnn. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7363–7372 (2019)
2019
Later among the works it cites.
Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., Lin, D.: Libra r-cnn: Towards balanced learning for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 821–830 (2019)
2019
Later among the works it cites.
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5693–5703 (2019)
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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)
2017
Cited alongside, same era.
Newell, A., Huang, Z., Deng, J.: Associative embedding: End-to-end learning for joint detection and grouping. In: Advances in neural information processing systems. pp. 2277–2287 (2017)
2017
Cited alongside, same era.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)
2017
Cited alongside, same era.
Tychsen-Smith, L., Petersson, L.: Denet: Scalable real-time object detection with directed sparse sampling. In: Proceedings of the IEEE international conference on computer vision. pp. 428–436 (2017)
2017
Cited alongside, same era.
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
Cited alongside, same era.
Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: European conference on computer vision. pp. 734–750 (2018)
2018
Cited alongside, same era.
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8759–8768 (2018)
2018
Cited alongside, same era.
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2403–2412 (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
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)
2019
Later among the works it cites.
Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2965–2974 (2019)
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: Proceedings of the IEEE International Conference on Computer Vision. pp. 9657–9666 (2019)
2019
Later among the works it cites.
Zhang, X., Wan, F., Liu, C., Ji, R., Ye, Q.: Freeanchor: Learning to match anchors for visual object detection. In: Advances in Neural Information Processing Systems. pp. 147–155 (2019)
2019
Later among the works it cites.
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
2019
Later among the works it cites.
Zhou, X., Zhuo, J., Krahenbuhl, P.: Bottom-up object detection by grouping extreme and center points. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 850–859 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Zhu, C., He, Y., Savvides, M.: Feature selective anchor-free module for single-shot object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 840–849 (2019)
2019
Later among the works it cites.
2020
Closest in time.
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing (2020)
2020
Closest in time.
Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 10781–10790 (2020)
2020
Closest in time.
Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9759–9768 (2020)
2020
Closest in time.