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We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category.
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Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K.: A large contextual dataset for classification, detection and counting of cars with deep learning. In: ECCV (2016)
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Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: CVPR (2016)
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Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV (2017)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L
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Babu Sam, D., Sajjan, N.N., Venkatesh Babu, R., Srinivasan, M.: Divide and grow: Capturing huge diversity in crowd images with incrementally growing cnn. In: CVPR (2018)
2018
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Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-shot object detection. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 384–400 (2018)
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Shi, M., Yang, Z., Xu, C., Chen, Q.: Revisiting perspective information for efficient crowd counting. In: CVPR (2019)
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Wan, J., Chan, A.: Adaptive density map generation for crowd counting. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1130–1139 (2019)
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Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: CVPR (2019)
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Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019)
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Zhang, A., Yue, L., Shen, J., Zhu, F., Zhen, X., Cao, X., Shao, L.: Attentional neural fields for crowd counting. In: ICCV (2019)
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Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: ECCV (2018)
2018
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Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S., Rajpoot, N., Shah, M.: Composition loss for counting, density map estimation and localization in dense crowds. In: ECCV (2018)
2018
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Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: CVPR (2018)
2018
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Liu, X., van de Weijer, J., Bagdanov, A.D.: Leveraging unlabeled data for crowd counting by learning to rank. In: CVPR (2018)
2018
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Liu, X., Van De Weijer, J., Bagdanov, A.D.: Leveraging unlabeled data for crowd counting by learning to rank. In: CVPR (2018)
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Lu, E., Xie, W., Zisserman, A.: Class-agnostic counting. In: ACCV (2018)
2018
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Rahman, S., Khan, S., Porikli, F.: Zero-shot object detection: Learning to simultaneously recognize and localize novel concepts. In: Asian Conference on Computer Vision. pp. 547–563. Springer (2018)
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Zhu, P., Wang, H., Saligrama, V.: Zero shot detection. IEEE Transactions on Circuits and Systems for Video Technology 30
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European conference on computer vision. pp. 213–229. Springer (2020)
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Liu, Y., Liu, L., Wang, P., Zhang, P., Lei, Y.: Semi-supervised crowd counting via self-training on surrogate tasks. In: European Conference on Computer Vision. pp. 242–259. Springer (2020)
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Ranjan, V., Wang, B., Shah, M., Hoai, M.: Uncertainty estimation and sample selection for crowd counting. In: ACCV (2020)
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2020
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Abousamra, S., Hoai, M., Samaras, D., Chen, C.: Localization in the crowd with topological constraints. In: AAAI (2021)
2021
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Ranjan, V., Sharma, U., Nguyen, T., Hoai, M.: Learning to count everything. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3394–3403 (2021)
2021
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Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3365–3374 (2021)
2021
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Wan, J., Liu, Z., Chan, A.B.: A generalized loss function for crowd counting and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1974–1983 (2021)
2021
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Wang, C., Song, Q., Zhang, B., Wang, Y., Tai, Y., Hu, X., Wang, C., Li, J., Ma, J., Wu, Y.: Uniformity in heterogeneity: Diving deep into count interval partition for crowd counting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3234–3242 (2021)
2021
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