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Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

Research 2024年第4期2024卷 533-544页

作者： Mianzhao Wang Fan Shi Xu Cheng Shengyong Chen The Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China

Pixel-level structure segmentations have attracted considerable attention,playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine ***,current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space,thereby limiting the capability of light field transmission for visual *** this paper,we propose a general light field modeling method for pixel-level structure segmentation,comprising a generative light field prompting encoder(LF-GPE)and a prompt-based masked light field pretraining(LF-PMP)*** LF-GPE,serving as a light field backbone,can extract both appearance and geometric structural cues *** aligns these features into a unified visual space,facilitating semantic ***,our LF-PMP,during the pretraining phase,integrates a mixed light field and a multi-view light field *** prioritizes considering the geometric structural properties of the light field,enabling the light field backbone to accumulate a wealth of prior *** evaluate our pretrained LF-GPE on two downstream tasks:light field salient object detection and semantic *** results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.

关键词： prompt backbone integrate

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GCA-Net: Global Cross Aware Network for salient object detection 23

GCA-Net: Global Cross Aware Network for salient object detec...

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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Zhang, Zixuan Shi, Fan Geng, Xinbo Tao, Yi He, Jing Key Laboratory of Computer Vision and System Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9781450399586

In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model's predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE. © 2023 ACM.

关键词： Semantics

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Dynamic Services Migration Based on Dueling Deep Q-Network in MEC 15

Dynamic Services Migration Based on Dueling Deep Q-Network i...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Zhang, Xinyang Bu, Chao School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education Tianjin China

ISBN: (纸本)9798350329360

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. © 2023 IEEE.

关键词： Markov processes

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Stackelberg Game-Driven Computational Offloading in Edge Computing Scenarios 15

Stackelberg Game-Driven Computational Offloading in Edge Com...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Zhang, Lan Bu, Chao School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education Tianjin China

ISBN: (纸本)9798350329360

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. © 2023 IEEE.

关键词： Computation offloading

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Adversarial image detection based on the maximum channel of saliency maps

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Optoelectronics Letters 2022年第5期18卷 307-312页

作者： FU Haoran WANG Chundong LIN Hao HAO Qingbo Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.

关键词： networks neural maps

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Subnetwork-based federated few-shot semantic segmentation of organ images

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Optoelectronics Letters 2025年

作者： Wu Junpeng Zhao Meng Zhang Huanping Key Laboratory of Computer Vision and System of Ministry of Education， School of Computer Science and Engineering， Tianjin University of Technology

Federated learning(FL)， as a distributed learning paradigm， allows multiple medical institutions to collaborate on learning without the need to centralize all client data. However， existing methods pay little attention to more challenging medical image semantic segmentation tasks， especially in the scenario of the imbalanced dataset in federated few-shot learning. In this paper， we propose a subnetwork-based federated few-shot organ image segmentation method. Firstly， individual clients train using local training samples and then upload local model gradients to the server. The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters. Through this method， we can learn the similarities between different clients to address data heterogeneity issues. In addition， to enhance the communication efficiency between clients and the server， we have also designed a personalized layer aggregation strategy， which only transmits partial layer model parameters during the communication process to improve communication efficiency. Finally， we conducted experiments on ABD-MRI and ABD-CT datasets to demonstrate the effectiveness of our method.

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Unsupervised image-to-image translation by semantics consistency and self-attention

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Optoelectronics Letters 2022年第3期18卷 175-180页

作者： ZHANG Zhibin XUE Wanli FU Guokai The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on generative adversarial nets(GANs). We use autoencoder structure to extract image features in the generator and add semantic consistency loss on extracted features to maintain the semantic consistency of the generated image. Self-attention mechanism at the end of generator is used to obtain long-distance dependency in image. At the same time, as expanding the convolution receptive field, the quality of the generated image is enhanced. Quantitative experiment shows that our method significantly outperforms previous works. Especially on images with obvious foreground, our model shows an impressive improvement.

关键词： translation features semantic

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An MCDM-Based Methodology for Influential Nodes Detection in a Social Network. Facebook as a Case Study

An MCDM-Based Methodology for Influential Nodes Detection in...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Qaffou, Issam Ait Rai, Khaoula LISI Laboratory Computer Science Department Faculty of Science Semlalia Cadi Ayyad University Marrakech Morocco Computer System and Vision Laboratory Faculty of Sciences Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783031263835

The identification of influential nodes in a social network is very important for many uses like the control of information spreading. This paper studies how a multi- criteria decision method (MCDM) can find influential persons in a social network. The empirical study performed on a Facebook dataset shows how we can visualize links between profiles and who are the top-k influential ones. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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MiT-Unet: Mixed Transformer Unet for Transmission Line Segmentation in UAV Images

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IEEE Transactions on Power Delivery 2025年第3期40卷 1279-1288页

作者： Wei, Ning Chen, Jianwei Sun, Shuifa The Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydro electric Engineering College of Computer and Information Technology China Three Gorges University Yichang443002 China The Engineering Research Center of Mobile Health Management System Ministry of Education Hangzhou Normal University China

The segmentation of power lines in drone images is one of the challenging tasks in the field of computer vision. Although power lines share the same difficulties with tiny object segmentation, occupying only a very small proportion of pixel areas in the images, the greater challenge is that they also have a very large visual perspective field. Therefore, the results obtained by traditional convolutional neural network-based segmentation methods are still unsatisfactory. To tackle these problems, we propose MiT-Unet (Mixed Transformer Unet), which has an analogous multi-level convolutional neural network structure similar to Unet for encoding and decoding transmission line detailed features. Meanwhile, dealing with excessive scales, we employ an Efficient Self-Attention-based module to enhance the global features of straight lines. Furthermore, in the level transition of this architecture, we propose simple yet efficient upsampling and downsampling modules, TLFM and TLFE, to fuse the global attention feature of power lines. These modules effectively extract and preserve the vulnerable features of power lines during the level transition process. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in transmission line segmentation on the public TTPLA dataset and PLDU dataset. Moreover, the computational efficiency of the proposed model makes it potentially deployable on mobile platforms. The testing code has been released at: https://***/xiachengs/MiT-Unet. ©1986-2012 IEEE.

关键词： Image segmentation

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Ultra-large mode area multi-core orbital angular momentum transmission fiber designed by neural network and optimization algorithms

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Optoelectronics Letters 2023年第12期19卷 744-751页

作者： GU Zhiwei HUANG Wei ZHANG Ran FAN Junjie SONG Binbin Key Laboratory of Computer Vision and Systems(Ministry of Education) School of Computer Science and EngineeringTianjin University of TechnologyTianjin 300384China Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

A large mode area multi-core orbital angular momentum(OAM)transmission fiber is designed and optimized by neural network and optimization *** neural network model has been established first to predict the optical properties of multi-core OAM transmission fibers with high accuracy and speed,including mode area,nonlinear coefficient,purity,dispersion,and effective index *** the trained neural network model is combined with different particle swarm optimization(PSO)algorithms for automatic iterative optimization of multi-core structures *** to the structural advantages of multi-core fiber and the automatic optimization process,we designed a number of multi-core structures with high OAM mode purity(>95%)and ultra-large mode area(>3000µm^(2)),which is larger by more than an order of magnitude compared to the conventional ring-core OAM transmission fibers.

关键词： network fiber OAM

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