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parallel Edge-image Learning for image Inpainting

Parallel Edge-Image Learning for Image Inpainting

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Hu, Junfeng Wang, Chengxin Zhang, Ying Liu, Li Yin, Yifang Zimmermann, Roger National University of Singapore Singapore Northwestern Polytechnical University China Chognqing University China Institute for Infocomm Research A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The primary goal of image inpainting is to fix holes in a damaged image with natural contents. A key challenge is that a damaged image contains complex structures in differ-ent ways, with each consisting of its configuration of edges and spatial dependencies. As a result, filled images often converge to unnatural and implausible results. Currently, the edge-image inpainting methods adopt two stages to recover edges and images successively, which suffer from feature in-consistency and error accumulation. This leads us to present a parallel edge-image learning framework that explicitly char-acterizes these internal configurations in a single stage. The framework introduces a dual parallel network-based decoder to generate the image and the edges concurrently, leading to feature consistency at the semantic level. Also, a new cross-fire mechanism aims to exchange edge-image information in the decoder, avoiding error accumulation. Empirical evaluations on benchmark datasets suggest that our approach out-performs the state-of-the-art methods on image inpainting. © 2022 IEEE.

关键词： Semantics

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A Cropping Attack Resistant Blind 3D Watermarking Algorithm

A Cropping Attack Resistant Blind 3D Watermarking Algorithm

引用

International conference on parallel distributed and Grid Computing (PDGC)

作者： Sumit Malik Sanjay Kumar Abhishek Sharma Department of Mathematics SCSET Bennett University Greater Noida Uttar Pradesh India SCSET Bennett University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The study introduces a 3D watermarking approach utilizing a modified Kirchhoff matrix and a visual security scheme. It demonstrates the approach’s resilience against cropping attacks through numerical experiments, reinforcing its effectiveness. The process of owner registration and identification involves several steps, such as 3D image processing, master and ownership share generation, and manipulation and restoration of the host object, depicted with illustrative images. The research highlights the practical application of the proposed scheme in automated vehicles, particularly in the context of their 3D mapping functionality.

关键词： Resistance Visualization Three-dimensional displays Watermarking Grid computing image restoration Object recognition Cryptography Resilience

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Fusion-Based Low-Light image Enhancement 29th

Fusion-Based Low-Light Image Enhancement

引用

29th International conference on MultiMedia Modeling (MMM)

作者： Wang, Haodian Wang, Yang Cao, Yang Zha, Zheng-Jun Univ Sci & Technol China Hefei 230027 Peoples R China

ISBN: (纸本)9783031270765;9783031270772

Recently, deep learning-based methods have made remarkable progress in low-light image enhancement. In addition to poor contrast, the images captured under insufficient light suffer from severe noise and saturation distortion. Most existing unsupervised learning-based methods adopt the two-stage processing method to enhance contrast and denoise sequentially. However, the noise will be amplified in the contrast enhancement process, thus increasing the difficulty of denoising. Besides, the saturation distortion caused by insufficient illumination is not considered well in existing unsupervised low-light enhancement methods. To address the above problems, we propose a novel parallel framework, which includes a saturation adaptive adjustment branch, brightness adjustment branch, noise suppression branch, and fusion module for adjusting saturation, correcting brightness, denoise, and multi-branch fusion, respectively. Specifically, the saturation is corrected via global adjustment, the contrast is enhanced through curve mapping estimation, and we use BM3D to preliminary denoise. Further, the enhanced branches are fed to the fusion module for a trainable guided filter, which is optimized in an unsupervised training manner. Experiments on the LOL, MIT-Adobe 5k, and SICE datasets demonstrate that our method achieves better quantitation and qualification results than the state-ofthe-art algorithms.

关键词： Unsupervised Low-light enhancement Noise suppression Saturation correction

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Subquadratic Kronecker Regression with Applications to Tensor Decomposition 36

Subquadratic Kronecker Regression with Applications to Tenso...

引用

36th conference on Neural Information processing Systems (NeurIPS)

作者： Fahrbach, Matthew Fu, Gang Ghadiri, Mehrdad Google Res Mountain View CA 94043 USA Georgia Tech Atlanta GA USA

ISBN: (纸本)9781713871088

Kronecker regression is a highly-structured least squares problem min(x) parallel to Kx-b parallel to(2)(2), where the design matrix K = A((1)) circle times center dot center dot center dot circle times A((N)) is a Kronecker product of factor matrices. This regression problem arises in each step of the widely-used alternating least squares (ALS) algorithm for computing the Tucker decomposition of a tensor. We present the first subquadratic-time algorithm for solving Kronecker regression to a (1+epsilon)-approximation that avoids the exponential term O(epsilon(-N)) in the running time. Our techniques combine leverage score sampling and iterative methods. By extending our approach to block-design matrices where one block is a Kronecker product, we also achieve subquadratic-time algorithms for (1) Kronecker ridge regression and (2) updating the factor matrices of a Tucker decomposition in ALS, which is not a pure Kronecker regression problem, thereby improving the running time of all steps of Tucker ALS. We demonstrate the speed and accuracy of this Kronecker regression algorithm on synthetic data and real-world image tensors.

关键词： Iterative methods

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QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT REINFORCEMENT LEARNING VALUE FUNCTION FACTORIZATION 47

QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT ...

引用

47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Shen, Siqi Liu, Jun Qiu, Mengwei Liu, Weiquan Wang, Cheng Fu, Yongquan Wang, Qinglin Qiao, Peng Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781665405409

The Centralized Training with Decentralized Execution paradigm (CTDE), which trains policies centrally with additional information, is important for Multi-Agent Reinforcement Learning (MARL). For CTDE, value function factorization methods make use of state during training and factorize the value function into multiple local value functions for decentralized execution. These approaches do not fully consider the relational information among agents, resulting in sub-optimal models for complex tasks. To remedy this issue, we propose QRelation which is a graph neural network approach for value function factorization. It considers both the static relations (e.g., agent types) and dynamic relations (e.g., close-by). We show that QRelation can obtain better results than state-of-the-art methods on challenging StarCraft II benchmarks.

关键词： Training conferences Reinforcement learning Benchmark testing Signal processing Graph neural networks Acoustics

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Late Fusion-based distributed Multimodal Learning

Late Fusion-based Distributed Multimodal Learning

引用

2023 IEEE International conference on Big Data, BigData 2023

作者： Giobergia, Flavio Baralis, Elena Politecnico di Torino Turin Italy

ISBN: (纸本)9798350324457

Multimodal artificial intelligence promises deeper insights by analyzing data from diverse sources such as text, images, audio and more. However, efficiently processing and fusing large multimodal datasets remains an open challenge. This paper presents a Spark-based approach to parallelize multimodal encoding tasks. A key aspect is the use of late fusion with frozen backbone encoders, allowing encodings to be processed independently across cluster nodes. The encoded vectors can then be used for a variety of supervised and unsupervised tasks, regardless of whether they are gradient-based or not. Experimental results on image, text and audio datasets show that Spark clusters can offer competitive performance compared to GPUs, especially for I/O-intensive modalities. While GPUs outperform the Spark cluster when sufficient CPU cores are available, Spark makes it possible to use already available commodity hardware. The presented architecture demonstrates how distributed computing platforms like Spark can be effectively 'repurposed' for multimodal AI, enhancing scalability and making such systems more accessible. © 2023 IEEE.

关键词： late fusion multimodal learning parallel encoding

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parallel K-means on GPU using Warp-Centric Strategies

Parallel K-means on GPU using Warp-Centric Strategies

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Michel B. Cordeiro Wagner M. Nunan Zola Federal University of Parana Curitiba Brazil

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

K-means clustering is a popular unsupervised machine learning method widely used in various applications, such as data mining, image processing, and social sciences. However, clustering can be computationally expensive, especially when applied to large datasets. In this article, we explore the performance optimization of k-means clustering through GPU parallelization strategies using the CUDA framework. Due to a warp-centric approach, the proposed implementation is named warp-KMeans. We have compared warp-KMeans to other best-in-class GPU implementations of the k-means algorithm, including the top-tier NVIDIA RAPIDS, a widely used set of libraries for high-performance data analysis on GPUs. Throughout our experiments, we observed that warp-KMeans excels in all scenarios, achieving an acceleration of up to 8.4 compared to the NVIDIA RAPIDS algorithm, when the number of centroids is low, while also exhibiting good scalability and up to 2.94 speedup, concerning various other dataset sizes, higher dimensional points, and number of centroids.

关键词： Machine learning algorithms Scalability Social sciences Memory management Graphics processing units Clustering algorithms Machine learning parallel processing Labeling Optimization

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DCSN: A Flexible and Efficient Lightweight Network for Dense Cell Segmentation 1

DCSN: A Flexible and Efficient Lightweight Network for Dense...

引用

1st IEEE International conference on Medical Artificial Intelligence, MedAI 2023

作者： Pan, Futai Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks. © 2023 IEEE.

关键词： Object detection

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Adaptive Progressive image Enhancement for Edge-Assisted Mobile Vision 18

Adaptive Progressive Image Enhancement for Edge-Assisted Mob...

引用

18th IEEE International conference on Mobility, Sensing and Networking (MSN)

作者： Feng, Daipeng Zeng, Liekang Pu, Lingjun Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China

ISBN: (纸本)9781665464574

Recent advances in deep learning models have pushed Super-Resolution (SR) techniques to an unprecedented altitude, enabling high-quality image rendering with variable scaling size and natural fidelity. To deploy them on resource-constrained mobile devices, however, confronts significant challenges of excessively long latency and poor user experience. To this end, we propose Apie, an edge-assisted adaptive image rendering system that allows low-latency, progressive image enhancement for a smooth user experience. Apie adopts a data parallel strategy across the end device and the edge server, along with a residual learning mechanism to judiciously retrieve information for SR models. Besides, a novel progressive image reconstruction is developed by exploiting content-aware image blocking and incremental image rendering, towards improved quality of user experience. Furthermore, Apie can dynamically adjust the choice of employed SR models with respect to the networking conditions, striking a good balance upon the latency-quality trade-off. Extensive evaluations show that Apie performs 7.33x faster than on-device GPU execution and 1.42x faster compared to the partial offloading method, while achieves 2.84dB higher PSNR compared to the interpolation method using conventional JPEG image compression and 0.74dB higher PSNR compared to the partial offloading method.

关键词： Edge intelligence image enhancement parallel processing user experience

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Lightweight Single image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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