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gFlow: Distributed Real-Time Reverse Remote Rendering System Model 31st

gFlow: Distributed Real-Time Reverse Remote Rendering System...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Xu, Yixiao Li, Yubo Xu, Wanzhao Gu, Yicheng Wang, Yun Ma, Jiangyuan Qi, Zhengwei Shanghai Jiao Tong University Shanghai China Lenovo Lanzhou China

ISBN: (纸本)9789819620609

Remote rendering has become increasingly important in the multimedia domain, driven by the rise of graphics-intensive applications and the need for efficient resource utilization. Traditional remote rendering focuses on scenarios where local devices lack GPUs, relying on remote machines equipped with GPUs for rendering. However, in emerging scenarios like autonomous driving, where local devices have high-performance GPUs and remote machines serve primarily as storage and management hubs, a concept we refer to as reverse remote rendering, academic research in this area remains limited. In this paper, we introduce gFlow, a distributed real-time reverse remote rendering system model that optimizes local GPU utilization by leveraging the VirtIO framework for efficient data transmission and remote control. Experiments show that our system achieved 10 times the FPS performance of the Linux approach x11 forwarding while maintaining comparable rendering quality to local processing in real-time remote game play. Additionally, gFlow demonstrates excellent scalability and resource utilization, making it a robust solution for distributed real-time reverse remote rendering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Rendering (computer graphics)

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Joint Image Super-Resolution and Low-Light Enhancement in the Dark 17th

Joint Image Super-Resolution and Low-Light Enhancement in ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Zhou, Feihu Chang, Kan Ling, Mingyang Li, Hengxin Xia, Shucheng School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Shenzhen Xiaomi Communications Co. Ltd. Shenzhen China

ISBN: (纸本)9789819609109

Existing super-resolution (SR) models are typically trained on datasets captured under normal-light conditions. However, when dealing with images captured under low-light conditions, the degradation becomes more complex, and the difference in lighting conditions often leads to low-quality results when using standard SR models. Due to error accumulation, simply cascading low-light enhancement (LE) and SR algorithms may not result in satisfactory results. Therefore, in this paper, we tackle this issue by jointly considering SR and LE. We first propose a new dataset called DarkSR, which contains low-resolution (LR) RAW and sRGB images captured under the low-light conditions, along with the corresponding high-resolution (HR) sRGB images captured under normal-light conditions. Noticing the linear relationship between pixel values and scene radiance, as well as the high bit depth of RAW images, and considering the presence of ISP pipeline-related information in sRGB images, we introduce JSLNet, a dual-input network that effectively explores the complementary information from the low-light LR RAW and sRGB images. Extensive experiments demonstrate that compared to other state-of-the-art (SOTA) methods, our method achieves the best quality of results, while maintaining a relatively low computational burden. The code and dataset are available at https://***/flyhu2/DarkSR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： HTTP

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3D-HRFC: 3D-Aware Image Generation at High Resolution with Faster Convergence 21st

3D-HRFC: 3D-Aware Image Generation at High Resolution with...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Xia, Qiqiang Chen, Junhong Li, Tianxiao Huang, Yiheng Asim, Muhammad Michiels, Nick Liu, Wenyin School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Hasselt University - Flanders Make - Expertise Centre for Digital Media Diepenbeek Belgium EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Zhongguancun Laboratory Beijing China

ISBN: (纸本)9789819601219

Learning 3D-aware generators from 2D image collections has attracted significant attention in the field of generative modeling. However, there are several challenges in generating high-resolution multi-view consistent images, e.g., 2D CNN-based approaches leverage upsampling layers to generate high-resolution images, easily resulting in inconsistencies across multi-view images;methods that generate images based on NeRF require tremendous memory space and a long time to converge. To this end, we propose a novel 3D-aware generative method named 3D-HRFC to generate high-resolution consistent images with faster convergence. Specifically, we first propose a depth fusion based super-resolution module that integrates the depth maps into the low-resolution images in order to generate consistent multi-view images. And then a skip super-resolution module is devised to enhance the generation of the high-resolution images. To generate high-resolution consistent images and accelerate the model convergence, we devise a composite loss function that consists of adversarial loss, super-resolution loss, and content consistency. Extensive experiments conducted on FFHQ and AFHQ-v2 Cats datasets illustrate that our proposed method can generate high-quality 3D-consistent images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D-aware Generation Feature Super-Resolution High-Resolution

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Exploring the Reliability of Foundation Model-Based Frontier Selection in Zero-Shot Object Goal Navigation 27th

Exploring the Reliability of Foundation Model-Based Fronti...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yuan, Shuaihang Unlu, Halil Utku Huang, Hao Wen, Congcong Tzes, Anthony Fang, Yi Abu Dhabi United Arab Emirates Electrical Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates Electrical and Computer Engineering Departement New York University BrooklynNY11201 United States Lab NYU Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781124

In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach introduces a multi-expert decision framework to address the nonsensical or irrelevant reasoning often seen in foundation model-based systems. The method comprises two key components: Diversified Expert Frontier Analysis (DEFA) and Consensus Decision Making (CDM). DEFA utilizes three expert models—furniture arrangement, room type analysis, and visual scene reasoning—while CDM aggregates their outputs, prioritizing unanimous or majority consensus for more reliable decisions. Demonstrating state-of-the-art performance on the RoboTHOR and HM3D datasets, our method excels at navigating towards untrained objects or goals and outperforms various baselines, showcasing its adaptability to dynamic real-world conditions and superior generalization capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision making

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SurfOcc: Surface-Based Feature Lifting for Vision-Centric 3D Occupancy Prediction 17th

SurfOcc: Surface-Based Feature Lifting for Vision-Centric 3...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Tonghui Gao, Zhi Lin, Zhipeng Liu, Xinyi Jin, Ronghe School of Remote Sensing and Information Engineering Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan430070 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong 999077 Hong Kong

ISBN: (纸本)9789819609710

3D occupancy prediction has been an emerging trend in 3D perception for its superiority in preserving exquisite geometric and semantic details. However, existing vision-based approaches either leave features unrefined or neglect depth ambiguity due to defective 2D-to-3D feature lifting modules, leading to imprecise prediction results. In this paper, we introduce SurfOcc, a vision-centric 3D occupancy prediction framework which addresses these limitations fundamentally. SurfOcc decouples the learning process of observed surfaces and occluded regions while seamlessly integrating them into an end-to-end architecture. Specifically, we first propose surface-based feature lifting to precisely locate observed surfaces and enhance the selected surface voxels via cross-attention during feature lifting. Then we design a feature diffuser which incorporates both local and global features to diffuse the reliable surface features to occluded regions. Experiments show that SurfOcc achieves state-of-the-art performance with 13.75 mIoU on SemanticKITTI and 42.38 mIoU on Occ3D-nuScenes, which also demonstrates the potential of SurfOcc in handling occlusion situations. Code is available at https://***/sullicsullic/SurfOcc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision

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Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification 17th

Cross Feature Fusion of Fundus Image and Generated Lesion ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Mok, Dahyun Bum, Junghyun Tai, Le Duc Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819609000

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model’s ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Cross-Domain Calibration and Boundary Denoising Network for Weakly Supervised Semantic Segmentation 27th

Cross-Domain Calibration and Boundary Denoising Network for...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Zhoufeng Li, Bingrui Ding, Shumin Xi, Jiangtao Li, Chunlei School of Information and Communication Engineering Zhongyuan University of Technology Henan450007 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia

ISBN: (纸本)9783031781216

Weakly Supervised Semantic Segmentation (WSSS) methods based on image-level labeling alleviate the burden of annotation due to image-level labels only providing object categories. However, the generated Class Activation Maps (CAM) can only localize the most discriminative region rather than the entire object region. To remedy this issue, a framework of WSSS algorithms for Cross-Domain Calibration and Boundary Denoising Network (CD-CBN) is presented. Specifically, a Spatial Feature Calibration Network (SFCN) is proposed to align cross-dimensional features with class prototypes, focusing on intra-class feature consistency. Then, a Class-Specific Distance Model (CSDM) is adopted to separate features from different classes, and feature activation in the object region surpasses the background area. Finally, a Full-domain-aware Noise Reduction Model (FNRM) is designed to refine the object boundary pixels by capturing global contextual features and further filtering out pixel-level noise. A comprehensive experimental evaluation of the highly challenging Pascal VOC 2012 dataset and MS COCO 2014 is presented in this study, illustrating the effectiveness of our suggested approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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FPIM: Fair and Privacy-Preserving Incentive Mechanism in Mobile Crowdsensing 24th

FPIM: Fair and Privacy-Preserving Incentive Mechanism in M...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lin, Ruonan Zhang, Yuanyuan Bi, Renwan Huang, Ruihong Xiong, Jinbo Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

ISBN: (纸本)9789819615476

Mobile crowdsensing (MCS) enables a group of mobile participants to use their intelligent sensing devices to complete the sensing tasks proposed by the task requester. The sensing data users submit is an important factor affecting the effectiveness of sensing task results. How to fairly incentivize users while protecting their privacy is an urgent problem that needs to be solved. Distributing user rewards based on data quality is the mainstream method for achieving fair incentives. However, most current work only uses a subjective approach to weigh different factors in data quality evaluation. Therefore, this paper proposes a fair and privacy-preserving incentive mechanism (FPIM) scheme based on data quality evaluation in MCS. Specifically, we introduce the entropy weight method into the MCS task to achieve objective weighting in secure data quality evaluation protocol by designing new secure interval determination protocol (SIDP) and secure compare protocol (SCP). Experimental results show that the computational cost and communication overhead of the proposed protocols are reduced by about 33% and 25% when compared with the existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Virtual Battlefields: The Rise of Video Games "Inspired" by War in Ukraine 13th

Virtual Battlefields: The Rise of Video Games "Inspired" by ...

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13th International Conference on Games and Learning Alliance, GALA 2024

作者： Shliakhovchuk, Elena Chover, Miguel The Polytechnic University of Valencia camino de Vera s/n Valencia46022 Spain Jaume I University of Castellón Vicent Sos Baynat s/n Castellón de la Plana12071 Spain

ISBN: (纸本)9783031782688

The intersection of video games and real-world global issues represents a growing field of interest within the academic community. This study contributes to the discourse by examining video games that incorporate narratives related to the war in Ukraine. From storyless simple arcades to richly detailed immersive RPGs, a diverse range of fifty-six games were examined from multiple perspectives to reflect on the unique lens through which players are invited to engage with the complexities of the Russian-Ukrainian War. The study analyses content, themes, and distinctive common features, considering the broader implications of using interactive media to engage with contemporary global conflicts. The findings reveal that these games transcend mere entertainment, serving as powerful mediums for conveying complex themes and educating players about significant world events. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Interactive computer graphics

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Semantic Segmentation Active Learning with Scene Coverage CoreSet 18th

Semantic Segmentation Active Learning with Scene Coverage C...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Liang, Hailun Qiang, Sunyuan Ma, Hui Wan, Jun Liang, Yanyan Macau University of Science and Technology China MAIS Institute of Automation Chinese Academy of Sciences Beijing China SAI University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819610709

Semantic segmentation typically necessitates laborious pixel-level annotations for training. In response, semantic segmentation active learning (SSAL) paradigms have emerged, aiming to reduce these costs by selecting the most valuable samples for annotation. Recent SSAL methods mainly prioritize evaluating sample annotation value based on data coverage. However, the overlooked poor data coverage during the cold start phase severely limits the annotation performance. To this end, this paper introduces a novel Scene Coverage CoreSet (SC-CoreSet) method to reduce coverage loss and boost active learning performance. Specifically, the proposed method enhances image representativeness by utilizing a scene-aware Gaussian mixture with well-established latent space to ensure the selection of the most representative images. Additionally, we integrate flood regularization derived from the Pareto frontiers to reduce coverage loss. Experiments on the Cambridge-driving Labeled Video Database dataset (Camvid) validate the superior performance of our proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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