In recent years, with the rapid development of deep learning, super-resolution methods based on convolutional neural networks (CNNs) have made great progress. However, the parameters and the required consumption of computing resources of these methods are also increasing to the point that such methods are difficult to implement on devices with low computing power. To address this issue, we propose a lightweight single image super-resolution network with an expectation-maximization attention mechanism (EMASRN) for better balancing performance and applicability. Specifically, a progressive multi-scale feature extraction block (PMSFE) is proposed to extract feature maps of different sizes. Furthermore, we propose an HR-size expectation-maximization attention block (HREMAB) that directly captures the long-range dependencies of HR-size feature maps. We also utilize a feedback network to feed the high-level features of each generation into the next generation’s shallow network. Compared with the existing lightweight single image super-resolution (SISR) methods, our EMASRN reduces the number of parameters by almost one-third. The experimental results demonstrate the superiority of our EMASRN over state-of-the-art lightweight SISR methods in terms of both quantitative metrics and visual quality. The source code can be downloaded at https://github.com/xyzhu1/EMASRN .

Stereo image super-resolution exploits additional features from cross view image pairs for high resolution (HR) image reconstruction. Recently, several new methods have been proposed to investigate cross view features along epipolar lines to enhance the visual perception of recovered HR images. Despite the impressive performance of these methods, global contextual features from cross view images are left unexplored. In this paper, we propose a cross view capture network (CVCnet) for stereo image super-resolution by using both global contextual and local features extracted from both views. Specifically, we design a cross view block to capture diverse feature embeddings from the views in stereo vision. In addition, a cascaded spatial perception module is proposed to redistribute each location in feature maps according to the weight it occupies to make the extraction of features more effective. Extensive experiments demonstrate that our proposed CVCnet outperforms the state-of-the-art image super-resolution methods to achieve the best performance for stereo image super-resolution tasks. The source code is available at https://github.com/xyzhu1/CVCnet .

Exploring the characterization laws of image data and improving the efficiency of image data characterization knowledge is essential to promote the development of the Internet of Things technology. Considering that images in the real world usually contain multiple objects, and the objects are closely dependent. For these reasons, it brings great challenges to the robust representation learning of multilabel images. In general, researchers model the relationship between objects based on a class activation map and use graph convolution to mine the dependencies between objects. However, graph structure data often contain noise, which means that the edges between nodes are sometimes not so reliable, and the relative importance of neighbors is also different. Based on this, our goal is to reduce noisy connections and false connections between objects, eliminate multilabel image representation bias, and learn robust representations. Therefore, we propose a robust representation learning method for multilabel images driven by graph attention network (RRL-GAT). Specifically, to reduce the accidental false connection of objects in the image, we propose the class attention graph convolution module (C-GAT) to mine the strong association structure between categories. Besides, for the dynamic correlation between objects in the image, we propose an adaptive graph attention convolution module (A-GAT) to capture the subtle dynamic dependencies in the image. The results on two authoritative data sets show that our method is significantly better than all current state-of-the-art methods. Besides, the visualization results show that RRL-GAT can capture the semantic relationship of a specific input image and has sufficient recognizability.

Although deep learning methods have drastically improved the performance on visual recognition tasks in which large inter-class variances exist, similar-class recognition continues to pose significant challenges, mainly due to the close resemblance between similar classes. The challenge is further compounded in the case of few-shot learning because only a very small amount of training data is available; accordingly, a certain performance degradation has been observed when some few-shot methods are applied for classification tasks. To address the aforementioned issue, we propose a novel Relation Separation Network (RSNet) in this paper, aiming to boost few-shot learning by improving similar-class recognition performance. We assume that image features consist of common and private features, where the common features capture the basic attributes shared among similar classes and their private counterparts capture the unique attributes of each class. Our RSNet learns to decouple the common and private features of an image. As a result, the feature representation of an image is composed of two weakly associated but easily aligned components, and better classification performance is achieved by giving more attention to subtle features. Experimental results on the publicly available datasets miniImageNet, CUB, and CIFAR-FS show that the proposed model outperforms existing state-of-the-art methods. Specifically, compared to PT+MAP, RSNet improves the accuracy of classification on the CUB dataset by approximately 5% and that of similar-class classification by more than 10%.

In recent years, novel view synthesis from a monocular image has become a research hot-spot that attracts significant attention. Some recent work identifies latent vectors for high-quality view generation via iterative optimisation, which is a time-consuming process. In contrast, some others utilise an encoder learning a mapping function to approximately estimate optimal latent codes, which significantly reduces its processing time but sacrifices reconstruction quality. Consequently, how to balance synthesis quality and its generation efficiency still remains challenging. In this paper, we propose a residual-based encoder to incorporate with a 3D Generative Adversarial Networks (GAN), named ReE3D, for novel view synthesis. It applies an iterative prediction of latent codes to ensure much higher quality of novel view synthesis with an insignificant increase of processing time when compared to existing encoder-based 3D GAN inversion methods. Additionally, we enforce a novel geometric loss constraint on the encoder to predict view-invariant latent codes, thus effectively mitigating the trade-off between geometric and texture quality in 3D GAN inversion. Extensive experimental results demonstrate that our extended encoder-based method has achieved best trade-off performance in terms of novel view synthesis quality and its execution time. Our method has gained comparable synthesis quality with exponentially decreased processing time when compared to iterative optimisation methods, while improved synthesis performance of encoder-based methods significantly.

How to use artificial intelligence technology to mine human abnormal behavior from considerable video data generated by the Internet of Things system has been intensively studied for a long time. Existing deep learning anomaly detection algorithms deployed in the cloud typically perform supervised learning based on constant kinds of abnormal behavior data. However, this supervised learning model with preset abnormal behavior categories ignores the diversity and unpredictability of abnormal occurrences in open scenarios. Thus, we propose an abnormal behavior detection algorithm as an edge network service by combining the advantages of cloud computing and the efficiency of edge networks. This method combines the double verification of global behavior detection and local fine-grained action cycle alignment to detect whether a behavior is abnormal. Moreover, to enable abnormal behavior detection models to predict test samples whose categories do not appear during the training stage, we propose an active label learning algorithm based on cycle clustering, which not only improves the efficiency of data transmission between the edge and the cloud but also makes model updates in the cloud more efficient. Extensive and quantitative experimental results show that our method can not only accurately detect abnormal human behavior at the edge of limited resources but also has strong robustness under the interference of test samples of unknown categories.