The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task.

In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-based clouds. In this paper, we propose a novel method named multi-evidence and multi-modal fusion network (MMFN) for ground-based cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from ground-based cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multi-modal network in MMFN learns multi-modal features for ground-based cloud. To fully fuse the multi-modal and multi-evidence visual features, we design two fusion layers in MMFN to incorporate multi-modal features with global and local visual features, respectively. Furthermore, we release the first multi-modal ground-based cloud dataset named MGCD which not only contains the ground-based cloud images but also contains the multi-modal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the state-of-the-art methods, which validates its effectiveness for ground-based cloud recognition.

Recently, multimodal cloud samples are utilized to learn completed feature representations for cloud classification. However, the existing methods neglect the related information from other multimodal cloud samples in the learning process, which leads to inadequate learning. In this article, we propose a novel deep model to learn heterogeneous deep features (HDFs) for multimodal ground-based remote sensing cloud classification. Specifically, we first design the convolutional neural network (CNN) extractor to combine the visual information and the multimodal information (MI) to obtain the CNN-based features of multimodal cloud samples. Afterward, we treat the CNN-based features of multimodal cloud samples as the nodes of graph, and utilize the similarity between nodes as the adjacency matrix. We feed the graph and the adjacency matrix into the graph convolutional network (GCN) extractor to obtain the GCN-based features that could capture correlations among multimodal cloud samples using graph convolutional layers. After obtaining CNN-based features and GCN-based features, we concatenate the two kinds of heterogeneous features to represent the multimodal cloud samples. As a result, the concatenated feature contains the visual information, the MI and the related information among multimodal cloud samples. We conduct a series of experiments on the multimodal ground-based cloud database (MGCD), and the experimental results verify that the proposed HDF outperforms state-of-the-art methods.

Most ground-based remote sensing cloud classification methods focus on learning representation features for cloud images while ignoring the correlations among cloud images. Recently, graph convolutional network (GCN) is applied to provide the correlations for ground-based remote sensing cloud classification, in which the graph convolutional layer aggregates information from the connected nodes of graph in a weighted way. However, the weights assigned by GCN cannot reflect the importance of connected nodes precisely, which declines the discrimination of the aggregated features (AFs). To overcome the limitation, in this article, we propose the context graph attention network (CGAT) for ground-based remote sensing cloud classification. Specifically, the context graph attention layer (CGA layer) of CGAT is proposed to learn the context attention coefficients (CACs) and obtain the AFs of nodes based on the CACs. We compute the CACs not only considering the two connected nodes but also their neighborhood nodes in order to stabilize the aggregation process. In addition, we propose to utilize two different transformation matrices to transform the node and its connected nodes into new feature spaces, which could enhance the discrimination of AFs. We concatenate the AFs with the deep features (DFs) as final representations for cloud classification. Since existing ground-based cloud data sets (GCDs) have limited cloud images, we release a new data set named GCD that is the largest one for ground-based cloud classification. We conduct a series of experiments on GCD, and the experimental results verify the effectiveness of CGAT.

Person re-identification (Re-ID) is a challenging task due to variations in pedestrian images, especially in cross-domain scenarios. The existing cross-domain person Re-ID approaches extract the feature from single pedestrian image, but they ignore the correlations among pedestrian images. In this paper, we propose Heterogeneous Convolutional Network (HCN) for cross-domain person Re-ID, which learns the appearance information of pedestrian images and the correlations among pedestrian images simultaneously. To this end, we first utilize Convolutional Neural Network (CNN) to extract the appearance features for pedestrian images. Then we construct a graph in the target dataset where the appearance features are treated as the nodes and the similarity represents the linkage between the nodes. Afterwards, we propose Dual Graph Convolution (DGConv) to explicitly learn the correlation information from the similar and dissimilar samples, which could avoid the over-smoothing caused by the fully connected graph. Furthermore, we design HCN as a multi-branch structure to mine the structural information of pedestrians. We conduct extensive evaluations for HCN on three datasets, i.e. Market-1501, DukeMTMC-reID and MSMT17, and the results demonstrate that HCN is superior to the state-of-the-art methods.

In this paper, we propose a novel deep model named integration convolutional neural network (ICNN) for person re-identification (re-ID) in camera networks, which jointly learns global and local features in a unified framework. To this end, the proposed ICNN simultaneously applies two kinds of loss functions. Specifically, we propose the soft triplet loss to learn global features which automatically adjusts the margin threshold within one batch. The soft triplet loss could alleviate the difficult in tuning parameters, and therefore learns discriminative global features. In order to avoid the part misalignment problem, we learn latent local features by conducting local horizontal average pooling on the convolutional maps. Afterwards, we implement the identification task on each local feature. We concatenate global and local features using a weighted strategy to present the pedestrian images. We evaluate the proposed ICNN on three large-scale databases. Our method achieves rank-1 accuracy of 92.13% on Market 1501, 61.4% on CUHK03 and 85.3% on DukeMTMC-reID, and the results outperform the state-of-the-art methods.