With the success of multimodal pre-training models in the video-language field and various downstream tasks, previous multimodal models used 3DCNN networks as video feature extractors, which have limitations in intera...
详细信息
Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of s...
Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model
Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in ...
详细信息
作者：
Chen JiaFan ShiXu ChengSchool of Computer Science and Engineering
The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) The Key Laboratory of Computer Vision and System (Ministry of Education) Tianjin University of Technology Tianjin China
4D light field imaging captures rich spatial-angular information, providing essential geometric cues for semantic segmentation tasks. In this paper, we introduce a novel backbone network called the Light Field Extract...
详细信息
ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
4D light field imaging captures rich spatial-angular information, providing essential geometric cues for semantic segmentation tasks. In this paper, we introduce a novel backbone network called the Light Field Extraction Interaction Network (LFEI-Net). LFEI-Net excels in extracting global structures and multi-scale spatial-angular features, capturing feature dependencies through channel modeling and diverse feature interactions. Unlike traditional methods that depend on pyramid and dilated feature extraction, LFEI-Net pioneers an efficient method by integrating large-scale horizontal depth-wise convolution (HDWC) and vertical depth-wise convolution (VDWC) with interactive operations for comprehensive spatial multi-scale feature extraction. Furthermore, we present the Multi-Angular Modeling (MAM) module, which effectively captures scene angle variations from multiple perspectives and precisely delineates object boundaries, thereby improving model adaptability. Our experimental evaluations on two datasets demonstrate that LFEI-Net significantly outperforms state-ofthe-art (SOTA) 2D and 4D light field semantic segmentation methods, achieving mean Intersection over Union (mIoU) of 83.72% and 86.88%, respectively.
Short-term aircraft trajectory prediction (TP) plays an important role in current air traffic control systems. However, existing works usually perform the multi-horizon TP task in an iterated manner which easily suffe...
详细信息
Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited objec...
详细信息
The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensi...
详细信息
The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computervision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. computervision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures.
With the rapid development of internet of everything, current Mobile Consumer Electronics (MCEs) can support complex computing due to their specialized data-handling capacity. Thus, federated Machine Learning (ML) and...
详细信息
With the number of users that use mobile devices for frequent transactions increasing rapidly, it is a great challenge to guarantee the credibility of transactions. Blockchain is regarded as a practical technology for...
With the number of users that use mobile devices for frequent transactions increasing rapidly, it is a great challenge to guarantee the credibility of transactions. Blockchain is regarded as a practical technology for such demand, however, the limited computing capacity of each user's device becomes a bottleneck. In this paper, the edge computing pattern is introduced to support complex computing for mobile devices of users by renting computing resource from computing service providers. By considering demands of both the user and the service provider, we propose a two-level game approach based on the Stackelberg Game for multiple users and multiple service providers on computing resources renting and pricing. The simulation results show that the proposed mechanism is feasible and effective.
As an extended computing paradigm of cloud computing, Mobile Edge Computing (MEC) facilitates real-time service responses by deploying resources near network edges. However, services should frequently move among multi...
As an extended computing paradigm of cloud computing, Mobile Edge Computing (MEC) facilitates real-time service responses by deploying resources near network edges. However, services should frequently move among multiple edge computing servers because of the mobility of most users, which accordingly leads to increased network operation costs and influences service quality. In this paper, we formulate the service migration problem as a Markov Decision Process (MDP) and introduce the dueling Deep Q-Network (DQN) to solve the problem, so as to reduce the network operating cost without lowering the service quality. We also propose a trajectory prediction approach to further optimize the service migration. Simulation experimental results demonstrate that the proposed mechanism can achieve a lower network operation cost without reducing the service quality.
暂无评论