Advances in Multimedia

Human abnormal action recognition is crucial for video understanding and intelligent surveillance. However, the scarcity of labeled data for abnormal human actions often hinders the development of high-performance models. Inspired by the multimodal approach, this paper proposes a novel approach that leverages text descriptions associated with abnormal human action videos. Our method exploits the correlation between the text domain and the video domain in the semantic feature space and introduces a multimodal heterogeneous transfer learning framework from the text domain to the video domain. The text of the videos is used for feature encoding and knowledge extraction, and knowledge transfer and sharing are realized in the feature space, which is used to assist in the training of the abnormal action recognition model. The proposed method reduces the reliance on labeled video data, improves the performance of the abnormal human action recognition algorithm, and outperforms the popular video-based models, particularly in scenarios with sparse data. Moreover, our framework contributes to the advancement of automatic video analysis and abnormal action recognition, providing insights for the application of multimodal methods in a broader context.

As virtual reality technology advances, 3D environment design and modeling have garnered increasing attention. Applications in networked virtual environments span urban planning, industrial design, and manufacturing, among other fields. However, existing 3D modeling methods exhibit high reconstruction error precision, limiting their practicality in many domains, particularly environmental design. To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN) algorithm for optimization and improvement. By employing the refined stereo-matching algorithm, a 3D reconstruction model was developed to augment 3D environment design and reconstruction accuracy while optimizing the 3D reconstruction effect. An experiment using the ShapeNet dataset demonstrated that the evaluation indices—Chamfer distance (CD), Earth mover’s distance (EMD), and intersection over union—of the model constructed in this study outperformed those of alternative methods. After incorporating the CNN module in the ablation experiment, CD and EMD increased by an average of 0.1 and 0.06, respectively. This validates that the proposed CNN module effectively enhances point cloud reconstruction accuracy. Upon adding the CNN module, the CD index and EMD index in the dataset increased by an average of 0.34 and 0.54, respectively. These results indicate that the proposed CNN module exhibits strong predictive capabilities for point cloud coordinates. Furthermore, the model demonstrates good generalization performance.

This paper addresses the problem of detecting internet rumors in social media. Rumors do great harm to information society, making rumor detection necessary. However, existing methods for detecting rumors generally only learn pattern features or text content features from the whole propagation process, which fall short in capturing multilevel features with topic relevance of text content from social media data. In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. We build a heterogeneous tweet graph for each single-level feature GCN to encode the topic relation among tweets based on the text contents. Experiments on real-world Twitter data demonstrate that our proposed approach achieves much better performance than the state-of-the-art methods with higher values of precision and recall as well as their corresponding F1 score. In addition, the diversity of our experimental results shows the generalization ability of our model.

Some time-consuming and labor-intensive techniques, like manual drawing or interactive modeling with an image editing system, are often used to show how a desert area might look after being transformed into a green field (oasis) in an image way. In order to improve the rendering efficiency of image style transformation and increase the variety of renderings, we can build an algorithm for automatically generating style images based on machine learning. In this paper, after comparing seven generative adversarial network (GAN) models in the way of theory analysis, we propose a method for generating green fields using desert images as input data, and a comprehensive comparison is presented on how GANs are currently applied to solve the desert-to-oasis problem. Experimental results show that two GAN models, geometrically consistent GAN and cyclic consistent GAN, have the best transfer effect of a desert image to oasis one in the view of quantitative indicators, Fréchet inception distance, and learned perceptual image patch similarity.

With the development of online social media, the number of various news has exploded. While social media provides an information platform for news release and dissemination, it also makes fake news proliferate, which may cause potential social risks. How to detect fake news quickly and accurately is a difficult task. The multimodal fusion fake news detection model is the current research focus and development trend. However, in terms of content, most existing methods lack the mining of background knowledge hidden in the news content and ignore the connection between background knowledge and existing knowledge system. In terms of the propagation chain, the research tends to emphasize only the single chain from the previous communication node, ignoring the intricate communication chain and the mutual influence relationship among users. To address these problems, this paper proposes a multimodal fake news detection model, A-KWGCN, based on knowledge graph and weighted graph convolutional network (GCN). The model fully extracted the features of the content and the interaction between users of the news dissemination. On the one hand, the model mines relevant knowledge concepts from the news content and links them with the knowledge entities in the wiki knowledge graph, and integrates knowledge entities and entity context as auxiliary information. On the other hand, inspired by the “similarity effect” in social psychology, this paper constructs a user interaction network and defines the weighted GCN by calculating the feature similarity among users to analyze the mutual influence of users. Two public datasets, Twitter15 and Twitter16, are selected to evaluate the model, and the accuracy reaches 0.905 and 0.930, respectively. In the comparison experiments, A-KWGCN model has more significant advantages than the other six comparison models in four evaluation indexes. Also, ablation experiments are conducted to verify that knowledge module and weighted GCN module play the significant role in the detection of fake news.

As the separation and carrier of indoor and outdoor energy and climate conditions, building skin plays an important role in indoor environment regulation and effective utilization of outdoor environmental resources. The traditional fixed skin of residential buildings in cold regions lacks the ability to respond to the external climate, so it is difficult to meet the dual requirements of building energy efficiency and indoor comfort. In the long river of architectural development, the most important thing of architectural design is how to meet the climate adaptability. Traditional architectural forms have long been unable to meet the current social development, climate conditions, and user needs. Based on the basic theory, this paper establishes a systematic understanding of inlay, studies the design method of complex skin with geometric algorithm as the operating tool, discusses the application of this method in architectural design in combination with practice, more systematically and comprehensively studies the building skin with dynamic climate adaptability, and makes a physical model of building skin with dynamic climate adaptability. The contrast experiments under different control modes were carried out using the climate chamber experimental system. This research focuses on taking geometric principles as the prototype, trying to break the common design idea of generating skin by overlapping cells, and providing a systematic skin design method with strong operability and modular structure, hoping to help expand creative thinking.