Abstract
Aiming at scale variation in crowd counting, we consider that an optimal solution is to take full advantage of the complementarity between multi-scale features. To implement this idea, we devise a Hierarchical Interweaved Aggregation Network (HIANet) in this paper. Instead of directly using the traditional concatenation method to aggregate multi-level features, our Hierarchical Interweaved Aggregation Module (HIAM) utilizes an innovative multiplication aggregation strategy to facilitate sufficient multi-scale feature fusion, which can help HIANet recover more structural and detail information. Furthermore, our HIANet employs a Gated Passing Mechanism (GPM) to selectively control the passing of feature information on each level, thus further to suppress the useless background information. Extensive experiments in different crowd scenes well demonstrate that our method has clear advantages under different evaluation metrics.
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This work is supported by the National Natural Science Foundation of China under Grant 61976127.
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Xie, J., Zheng, J., Gu, L., Lyu, C., Lyu, L. (2021). HIANet: Hierarchical Interweaved Aggregation Network for Crowd Counting. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_66
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DOI: https://doi.org/10.1007/978-3-030-92310-5_66
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