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Traffic situation generation is of importance in the intelligent transportation field, evaluating and simulating the macroscopic traffic conditions. The government often uses the historical traffic data on the same weekday to analyze the future traffic situations, which works unfavorably due to some traffic-related information deficiency, such as weather, location, traffic accidents, social activities and so on. Therefore, how to accurately generate the traffic situation is a challenging problem. Fortunately, massive traffic-related information spread in social media often indicates the traffic situation variation trend, which provides the sufficient information for the traffic situation generation. In this paper, we propose a novel Text-to-Traffic generative adversarial network framework ($\text{T}^{2}$ GAN), which fuses the traffic data and the semantic information collected from social media to generate the traffic situation. To reduce the huge gap between the above two modalities and improve the authenticity of the generated traffic situation, we raise a global-local loss. Additionally, we build a heterogeneous dataset containing the traffic-related text data collected from social media and the corresponding traffic passenger flow data. Experimental results show that the proposed methods are obviously better than many outstanding traffic situation generation methods based on neural networks. [ABSTRACT FROM AUTHOR]

With the popularity of cameras and sensors, massive data are captured from various view angles or modalities, which provide abundant complementary information and also bring great challenges for traditional clustering methods. In this article, we propose a novel Adaptive K-Multiple-Means for multi-view clustering method (AKM3C). Unlike traditional multi-view K-means methods by grouping samples into $C$ clusters each with a cluster center in every view, the proposed AKM3C employs $M (M>C)$ sub-cluster centers in each view to reveal the sub-cluster structure in the multi-view data thus enhances the clustering performance. Additionally, to distinguish the importance of different views, instead of using empirical weights, AKM3C exploits the multi-view combination weights strategy to assign a weight to each view automatically and thus fuses the complementary information of different views properly to get an optimally shared bipartite graph, on which the Laplacian rank constraint is executed and the final clusters are obtained by directly partitioning. An efficient optimization algorithm proposed with complexity and convergence analysis is used to solve the proposed AKM3C method. The extensive experimental results on eight public datasets show that the proposed AKM3C performs better than state-of-the-art multi-view clustering methods. The code can be downloaded at https://drive.google.com/file/d/1CQ0royrYxKFJdNLnbBQSbDrohtfH71di/view?usp=sharing. [ABSTRACT FROM AUTHOR]

Visual attention has been successfully applied in image captioning to selectively incorporate the most relevant areas to the language generation procedure. However, the attention in current image captioning methods is only guided by the hidden state of language model, e.g. LSTM (Long-Short Term Memory), indirectly and implicitly, and thus the attended areas are weakly relevant at different time steps. Besides the spatial relationship of attention areas, the temporal relationship in attention is crucial for image captioning according to the attention transmission mechanism of human vision. In this paper, we propose a new spatio-temporal memory attention (STMA) model to learn the spatio-temporal relationship in attention for image captioning. The STMA introduces the memory mechanism to the attention model through a tailored LSTM, where the new cell is used to memorize and propagate the attention information, and the output gate is used to generate attention weights. The attention in STMA transmits with memory adaptively and dependently, which builds strong temporal connections of attentions and learns the spatio-temporal relationship of attended areas simultaneously. Besides, the proposed STMA is flexible to combine with attention-based image captioning frameworks. Experiments on MS COCO dataset demonstrate the superiority of the proposed STMA model in exploring the spatio-temporal relationship in attention and improving the current attention-based image captioning. [ABSTRACT FROM AUTHOR]

Subspace data representation has recently become a common practice in many computer vision tasks. Low-rank representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. This paper explores the possibility of extending LRR for subspace data on Grassmann manifold. Rather than directly embedding the Grassmann manifold into the symmetric matrix space, an extrinsic view is taken to build the self-representation in the local area of the tangent space at each Grassmannian point, resulting in a localized LRR method on Grassmann manifold. A novel algorithm for solving the proposed model is investigated and implemented. The performance of the new clustering algorithm is assessed through experiments on several real-world data sets including MNIST handwritten digits, ballet video clips, SKIG action clips, and DynTex++ data set and highway traffic video clips. The experimental results show that the new method outperforms a number of state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

In multicamera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering. In this paper, a novel representation for multicamera video data, namely, the product Grassmann manifold (PGM), is proposed to model video sequences as points on the Grassmann manifold and integrate them as a whole in the product manifold form. In addition, with a new geometry metric on the product manifold, the conventional low rank representation (LRR) model is extended onto PGM and the new LRR model can be used for clustering nonlinear data, such as multicamera video data. To evaluate the proposed method, a number of clustering experiments are conducted on several multicamera video data sets of human activity, including the Dongzhimen Transport Hub Crowd action data set, the ACT 42 Human Action data set, and the SKIG action data set. The experiment results show that the proposed method outperforms many state-of-the-art clustering methods. [ABSTRACT FROM PUBLISHER]