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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

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High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

A Novel Dual-Stage Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs

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SSRN

SSRN 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most signiﬁcant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Speciﬁcally, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modiﬁed benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

A novel dual-stage algorithm for capacitated arc routing problems with time-dependent service costs

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arXiv 2024年

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most significant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Specifically, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modified benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

Agent-SiMT: Agent-Assisted Simultaneous Translation With Large Language Models

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IEEE Transactions on Audio, Speech and Language Processing

IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 2074-2083页

作者： Shoutao Guo Shaolei Zhang Zhengrui Ma Min Zhang Yang Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (ICT/CAS) Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of AI Safety Chinese Academy of Sciences Beijing China

Simultaneous Machine Translation (SiMT) generates target translations in real-time while reading the source sentence. It relies on a policy to determine the optimal timing for producing translations, aiming to achieve both low response latency and high translation quality. Existing SiMT methods typically utilize the traditional Transformer architecture, employing complicated dynamic programming algorithms to jointly optimize policy decisions and translation generation. While these methods excel at determining policies, their translation capabilities are suboptimal. In contrast, Large Language Models (LLMs), trained on extensive corpora, exhibit exceptional generation capabilities but struggle to learn translation policies through traditional Transformer-based training methods. To address these limitations, we introduce Agent-SiMT, a novel framework combining the strengths of LLMs and traditional SiMT methods. Agent-SiMT contains a policy-decision agent and a translation agent. The policy-decision agent is managed by a SiMT model, which determines the policy using partial source sentences and translations. The translation agent, powered by an LLM, generates the target translation according to the policy decisions. These two agents work collaboratively to perform SiMT. Experiments demonstrate that our method attains state-of-the-art performance.

关键词： Translation Hidden Markov models Transformers Training Large language models Collaboration Vocabulary Speech processing Memory modules Machine translation

Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

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arXiv 2022年

作者： Jiang, Xuhao Tan, Weimin Cheng, Ri Zhou, Shili Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods. © 2022, CC BY.

关键词： Textures

SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION WITH CO-DISTRIBUTION ALIGNMENT

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arXiv 2023年

作者： Wang, Tao Huang, Zhongzheng Wu, Jiawei Cai, Yuanzheng Li, Zuoyong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University China College of Computer and Data Science Fuzhou University China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China School of Electrical and Mechanical Engineering Fujian Agriculture and Forestry University China

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

Multipath Components Modeling and Measuring in Device-Free Underground Parking Localization

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Multipath Components Modeling and Measuring in Device-Free U...

IEEE International Conference on Communications in China (ICCC)

作者： Sihai Yang Xing Xiao Xing Wang Zhuoting Chen Menggang Sheng Zhiqiang Yao School of Automation and Electronic Information Xiangtan University Xiangtan China Hunan Province Key Laboratory of Credible Intelligent Navigation and Positioning Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan China School of Mathematics and Computational Science Xiangtan University Xiangtan China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The Device-Free Localization (DFL) system hold significant application value in NLOS (Non-Line-of-Sight) environments where GPS signals are unavailable. It is a challenge to accurately distinguish whether the changes in spatial signals are caused by the presence of users or the changes of environment, particularly in Non-Line-of-Sight (NLOS) environments without GPS signals. To address this issue, we proposes a DFL method utilizing Multipath Components (MPCs), which involves a detailed analysis of how the presence of users affects the MPCs. Specifically, a calculation method for signal propagation paths using virtual anchors has been developed to accurately construct a user-based diffraction model. An algorithm for extracting MPCs from actual signal measurements was also proposed. The maximum average absolute error between the simulated and measured MPC amplitudes with UWB was 0.9461 dB, validating the possibility of substituting simulation models for actual measurements. Moreover, by utilizing a deep learning network with MPCs as input features, the DFL method achieves a positioning accuracy of 0.44 m in an underground parking with only two sensors. This represents an improvement of $\mathbf{0. 3 5 m}$ over traditional RSS-based fingerprinting localization methods.

关键词： Location awareness Deep learning Accuracy Diffraction Measurement uncertainty Sensor phenomena and characterization Fingerprint recognition

Multi-Scale Structure-Guided Graph Generation for Multi-View Semi-Supervised Classification

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SSRN

SSRN 2024年

作者： Wu, Yilin Chen, Zhaoliang Zou, Ying Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science Hong Kong Baptist University Hong Kong

Graph convolutional network has emerged as a focal point in machine learning because of its robust graph processing capability. Most existing graph convolutional network-based approaches are designed for single-view data, yet in many practical scenarios, data is represented through multiple views. Although the ability of graph convolutional network is undeniable, the quality of graph directly affects its performance. Moreover, the process of graph generation with traditional nearest neighbor in traditional multi-view learning actually amplifies the influence of different information on performance across these views. To tackle the aforementioned challenges, this paper proposes a multi-scale graph generation deep learning framework, called multi-scale semi-supervised graph generation based multi-view classification, consisting of two modules: edge sampling and path sampling. The former aims to generate an adjacency graph by selecting edges based on the maximum likelihood among graphs from different views. Meanwhile, the latter seeks to construct adjacency graphs according to the characteristics of paths within the graphs. Extensive experimental results robustly demonstrate the superior performance of our proposed framework, compared to other state-of-the-art multi-view semi-supervised approaches. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

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arXiv 2022年

作者： Cheng, Ri Sun, Yuqi Yan, Bo Tan, Weimin Ma, Chenxi School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims to increase the resolution of multiview images captured from the same scene. One solution is to apply image or video super-resolution (SR) methods to reconstruct HR results from the low-resolution (LR) input view. However, these methods cannot handle large-angle transformations between views and leverage information in all multi-view images. To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view. Specifically, the proposed Geometry-Aware Reference Synthesis module in MVSRnet uses geometry information and all multi-view LR images to synthesize pixel-aligned HR reference images. Then, the proposed Dynamic High-Frequency Search network fully exploits the high-frequency textural details in reference images for SR. Extensive experiments on several benchmarks show that our method significantly improves over the state-of-the-art approaches. © 2022, CC BY.

关键词： Geometry