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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. [ABSTRACT FROM AUTHOR]

Weather classification from single images is significant for many outdoor computer vision applications, while it has not been thoroughly studied. Existing methods view it as a supervised task under the guidance of weather labels. Though these methods have made great success, they are not applicable to real-world applications because of their reliance on massive human-annotated weather images. In this paper, we take the initial attempt to treat weather classification as an unsupervised task, i.e. to classify weather conditions from unlabeled outdoor images. Specifically, we propose a three-step unsupervised approach to automatically group images into weather clusters. In the first step, i.e. weather feature learning, semantically meaningful weather features are learned by employing a self-supervised task. Furthermore, we also introduce online triplet mining into the self-supervised task to extract discriminative weather features. In the second step, i.e. weather clustering, we design a learnable clustering method via mining the nearest neighbors and enforcing consistency between each sample and its nearest neighbors. To alleviate the uncertainty of weather clustering due to noisy nearest neighbors, we propose a self-labeling method as the third step to employ the already well-classified samples for fine-tuning. Experimental results on two publicly available weather image datasets demonstrate that the proposed approach achieves promising performance. [ABSTRACT FROM AUTHOR]