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We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach.
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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
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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)
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Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European conference on computer vision. pp. 483–499. Springer (2016)
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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
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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)
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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)
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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)
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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)
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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)
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Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263–7271 (2017)
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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)
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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)
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Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 734–750 (2018)
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Dong, Z., Li, G., Liao, Y., Wang, F., Ren, P., Qian, C.: Centripetalnet: Pursuing high-quality keypoint pairs for object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10519–10528 (2020)
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Ke, W., Zhang, T., Huang, Z., Ye, Q., Liu, J., Huang, D.: Multiple anchor learning for visual object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10206–10215 (2020)
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Kim, K., Lee, H.S.: Probabilistic anchor assignment with iou prediction for object detection. In: European Conference on Computer Vision. pp. 355–371. Springer (2020)
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2018
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Tychsen-Smith, L., Petersson, L.: Improving object localization with fitness nms and bounded iou loss. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6877–6885 (2018)
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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
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Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 658–666 (2019)
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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
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Yi, J., Wu, P., Metaxas, D.N.: Assd: Attentive single shot multibox detector. Computer Vision and Image Understanding 189
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Zhang, X., Wan, F., Liu, C., Ji, R., Ye, Q.: Freeanchor: Learning to match anchors for visual object detection. Advances in neural information processing systems 32
2019
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Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
2019
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Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29
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Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H.: Cspnet: A new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp. 390–391 (2020)
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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)
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Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. In: AAAI. pp. 12993–13000 (2020)
2020
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Ge, Z., Liu, S., Li, Z., Yoshie, O., Sun, J.: Ota: Optimal transport assignment for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 303–312 (2021)
2021
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Tang, Z., Yang, J., Pei, Z., Song, X.: Coordinate-based anchor-free module for object detection. Applied Intelligence pp. 1–15 (2021)
2021
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Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: Varifocalnet: An iou-aware dense object detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8514–8523 (2021)
2021
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