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Graph convolutional networks with attention for multi-label weather recognition

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Abstract

Weather recognition is a significant technique for many potential computer vision applications in our daily lives. Generally, most existing works treat weather recognition as a single-label classification task, which cannot describe the weather conditions comprehensively due to the complex co-occurrence dependencies between different weather conditions. In this paper, we propose a novel Graph Convolution Networks with Attention (GCN-A) model for multi-label weather recognition. To our best knowledge, this is the first attempt to introduce GCN into weather recognition. Specifically, we employ GCN to capture weather co-occurrence dependencies via a directed graph. The graph is built over weather labels, where each node (weather label) is represented by word embeddings of a weather label. Furthermore, we design a re-weighted mechanism to build weather correlation matrix for information propagation among different nodes in GCN. In addition, we develop a channel-wise attention module to extract informative semantic features of weather for effective model training. Compared with the state-of-the-art methods, experiment results on two widely used benchmark datasets demonstrate that our proposed GCN-A model achieves promising performance.

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Acknowledgements

This work is supported by the National Key R&D Program of China (2019YFC1408405), National Natural Science Foundation of China (No.61672475, 61872326).

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, 266000, China

    Kezhen Xie, Zhiqiang Wei, Lei Huang, Qibing Qin & Wenfeng Zhang

  2. Pilot National Laboratory for Marine Science and Technology, Qingdao, China

    Zhiqiang Wei & Lei Huang

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  1. Kezhen Xie
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  2. Zhiqiang Wei
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Correspondence to Lei Huang.

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Xie, K., Wei, Z., Huang, L. et al. Graph convolutional networks with attention for multi-label weather recognition. Neural Comput & Applic 33, 11107–11123 (2021). https://doi.org/10.1007/s00521-020-05650-8

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