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Scene classification has established itself as a challenging research problem. Compared to images of individual objects, scene images could be much more semantically complex and abstract. Their difference mainly lies in the level of granularity of recognition. Yet, image recognition serves as a key pillar for the good performance of scene recognition as the knowledge attained from object images can be used for accurate recognition of scenes. The existing scene recognition methods only take the category label of the scene into consideration. However, we find that the contextual information that contains detailed local descriptions are also beneficial in allowing the scene recognition model to be more discriminative. In this paper, we aim to improve scene recognition using attribute and category label information encoded in objects. Based on the complementarity of attribute and category labels, we propose a Multi-task Attribute-Scene Recognition (MASR) network which learns a category embedding and at the same time predicts scene attributes. Attribute acquisition and object annotation are tedious and time consuming tasks. We tackle the problem by proposing a partially supervised annotation strategy in which human intervention is significantly reduced. The strategy provides a much more cost-effective solution to real world scenarios, and requires considerably less annotation efforts. Moreover, we re-weight the attribute predictions considering the level of importance indicated by the object detected scores. Using the proposed method, we efficiently annotate attribute labels for four large-scale datasets, and systematically investigate how scene and attribute recognition benefit from each other. The experimental results demonstrate that MASR learns a more discriminative representation and achieves competitive recognition performance compared to the state-of-the-art methods.

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.

Person re-identification (re-id) is a cross-camera retrieval task which establishes a correspondence between images of a person from multiple cameras. Deep learning methods have been successfully applied to this problem and have achieved impressive results. However, these methods require a large amount of labeled training data. Currently, the labeled datasets in person re-id are limited in their scale and manual acquisition of such large-scale datasets from surveillance cameras is a tedious and labor-intensive task. In this paper, we propose a framework that performs intelligent data augmentation and assigns the partial smoothing label to generated data. Our approach first exploits the clustering property of existing person re-id datasets to create groups of similar objects that model cross-view variations. Each group is then used to generate realistic images through adversarial training. Our aim is to emphasize the feature similarity between generated samples and the original samples. Finally, we assign a non-uniform label distribution to the generated samples and define a regularized loss function for training. The proposed approach tackles two problems 1) how to efficiently use the generated data and 2) how to address the over-smoothness problem found in current regularization methods. The extensive experiments on four large-scale datasets show that our regularization method significantly improves the re-id accuracy compared to existing methods.

Person re-identification aims to identify pedestrians across non-overlapping camera views. Deep learning methods have been successfully applied to solving the problem and have achieved impressive results. However, these methods rely either on feature extraction or metric learning alone ignoring the joint benefit and mutual complementary effects of the person view-specific representation. In this paper, we propose a multi-view deep network architecture coupled with n-pair loss (JNPL) to eliminate the complex view discrepancy and learn nonlinear mapping functions that are view-invariant. We show that the problem of the large variation in viewpoints of a pedestrian can be well solved using a multi-view network. We simultaneously exploit the complementary representation shared between views and propose an adaptive similarity loss function to better learn a similarity metric. In detail, we first extract view-invariant feature representation from n-pair of images using multi-stream CNN and then aggregate these features for predictions. Given n-positive pairs and a negative example, the network aggregate the feature map of the n-positive pairs and predicts the identity of the person and at the same time learns features that discriminate positive pairs against the negative sample. Extensive evaluations on three large scale datasets demonstrate the substantial advantages of our method over existing state-of-art methods.

In this paper, we presented models and software for spam recognition using an improved Bayesian filtering technique. Based on a corpus from Androutsopoulos et al, our Spam Recognition framework outperforms other state-of-the-art learning methods based on Bayesian algorithm in terms of spam detection capability. Our software has proved an accuracy of 99.9% of good classification. The 0.1% of other messages have been classify as "may be spam" due to their vagueness signature. Brief, in the case of extremely high misclassification cost, our model still remains stable accuracy with low computation cost, while other methods' performance deteriorates significantly as the cost factor increases. [ABSTRACT FROM AUTHOR]