In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the...
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Breakthroughs in natural language processing (NLP) by large-scale language models (LLMs) have led to superior performance in multilingual tasks such as translation, summarization, and Q&A. However, the size and co...
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The proceedings contain 77 papers. The special focus in this conference is on parallelprocessing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Mult...
ISBN:
(纸本)9783031304446
The proceedings contain 77 papers. The special focus in this conference is on parallelprocessing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs;Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-parallel Applications;Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure;building a Fine-Grained Analytical Performance Model for Complex Scientific Simulations;evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs;Smart Clustering of HPC Applications Using Similar Job Detection methods;distributed Work Stealing in a Task-Based Dataflow Runtime;task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning;Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures;General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case;proactive Task Offloading for Load Balancing in Iterative Applications;language Agnostic Approach for Unification of Implementation Variants for Different Computing Devices;high Performance Dataframes from parallelprocessing Patterns;global Access to Legacy Data-Sets in Multi-cloud Applications with Onedata;MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms;Breaking Down the parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software;GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM;a Novel parallel Approach for Modeling the Dynamics of Aerodynamically Interacting Particles in Turbulent Flows;reliable Energy Measurement on Heterogeneous Systems–on–Chip Based Environments;distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans;a Generalized parallel Prefix Sums Algorithm for Arbitrary Size Arrays;GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations;Ant System Inspired Heuristic Optimization of UAVs Depl
recently monitoring and locating of civil unmanned aerial vehicles (UAVs) have become research hotspots as the rapid development and application of various UAVs brings increasingly severe safety threaten. The image tr...
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Accurately evaluating the defense models against adversarial examples has been proven to be a challenging task. We have recognized the limitations of mainstream evaluation standards, which fail to account for the disc...
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ISBN:
(数字)9789819708086
ISBN:
(纸本)9789819708079;9789819708086
Accurately evaluating the defense models against adversarial examples has been proven to be a challenging task. We have recognized the limitations of mainstream evaluation standards, which fail to account for the discrepancies in evaluation results arising from different adversarial attack methods, experimental setups, and metrics sets. To address these disparities, we propose the Composite Multidimensional Model Robustness (CMMR) evaluation framework, which integrates three evaluation dimensions: attack methods, experimental settings, and metrics sets. By comprehensively evaluating the model's robustness across these dimensions, we aim to effectively mitigate the aforementioned variations. Furthermore, the CMMR framework allows evaluators to flexibly define their own options for each evaluation dimension to meet their specific requirements. We provide practical examples to demonstrate how the CMMR framework can be utilized to assess the performance of models in enhancing robustness through various approaches. The reliability of our methodology is assessed through both practical examinations and theoretical validations. The experimental results demonstrate the excellent reliability of the CMMR framework and its significant reduction of variations encountered in evaluating model robustness in practical scenarios.
This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e.g., perspective crops from a panorama or multi-vie...
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ISBN:
(纸本)9781713899921
This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e.g., perspective crops from a panorama or multi-view images given depth maps and poses). Unlike prior methods that rely on iterative image warping and inpainting, MVDiffusion simultaneously generates all images with a global awareness, effectively addressing the prevalent error accumulation issue. At its core, MVDiffusion processes perspective images in parallel with a pre-trained text-to-image diffusion model, while integrating novel correspondence-aware attention layers to facilitate cross-view interactions. For panorama generation, while only trained with 10k panoramas, MVDiffusion is able to generate high-resolution photorealistic images for arbitrary texts or extrapolate one perspective image to a 360-degree view. For multi-view depth-to-image generation, MVDiffusion demonstrates state-of-the-art performance for texturing a scene mesh. The project page is at https://***/.
Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have been directed toward reducing the numb...
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ISBN:
(纸本)9781713899921
Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have been directed toward reducing the number of denoising steps, but these methods hurt sample quality. Instead of reducing the number of denoising steps (trading quality for speed), in this paper we explore an orthogonal approach: can we run the denoising steps in parallel (trading compute for speed)? In spite of the sequential nature of the denoising steps, we show that surprisingly it is possible to parallelize sampling via Picard iterations, by guessing the solution of future denoising steps and iteratively refining until convergence. With this insight, we present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel. ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPMSolver. Using ParaDiGMS, we improve sampling speed by 2-4x across a range of robotics and image generation models, giving state-of-the-art sampling speeds of 0.2s on 100-step DiffusionPolicy and 14.6s on 1000-step StableDiffusion-v2 with no measurable degradation of task reward, FID score, or CLIP score.
To meet the high throughput and stringent Quality of Service (QoS) requirements of the fifth and the sixth generation (5G and 6G) users, operators have explored technologies such as Multi-Input Multi-Output (MIMO), ma...
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ISBN:
(纸本)9798350385939;9798350385922
To meet the high throughput and stringent Quality of Service (QoS) requirements of the fifth and the sixth generation (5G and 6G) users, operators have explored technologies such as Multi-Input Multi-Output (MIMO), massive MIMO (mMIMO), Base Station (BS) densification, Non-Orthogonal Multiplex Access (NOMA) etc. The Central Unit (CU) and distributed Unit (DU) split architectures defined in Open Radio Access Network (ORAN) and 3rd Generation Partnership Project (3GPP), gives the possibilities to explore the software-based methods, which were restricted earlier due to hardware-centric systems. With software defined Radio Access Network (RAN) at cloud and artificial intelligence being a promising domain for solving various problems, these two can be leveraged in exploring to solve or optimize various existing challenges like channel estimation in the wireless receivers, which is very computationally expensive. In this work, we attempted to improve the performance of the compute intensive module channel estimation using machine learning architecture. Convolutional Neural Network (CNN) based imageprocessing architectures, Super Resolution CNN (SRCNN) and Denoising CNN (DnCNN) were implemented for channel estimation module, along with various combinations of spatial data arrangements of the input signal. The spatial data arrangement of complex and real components of input signal outperformed and yielded better accuracy compared to traditional methods such as estimated Minimum Mean Square Error (MMSE) and Approximate Linear Minimum Mean Square Error (ALMMSE).
For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is ...
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Removing noise in digital images is a fundamental operation that arises in many application domains. In this paper we consider the median filter, a filtering technique that replaces the color of each pixel with the me...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
Removing noise in digital images is a fundamental operation that arises in many application domains. In this paper we consider the median filter, a filtering technique that replaces the color of each pixel with the median of those in a square neighborhood of fixed radius. For some use cases, the size of the neighborhood or the image depth may be large, making existing algorithms either too slow, or not applicable at all due to excessive memory requirements. In this paper we describe architecture-specific optimizations that enable the computation of the median filter with arbitrary window size and image depth on multicore processors and GPUs. We report preliminary results that indicate that the parallel implementations are suitable for practical use, with the GPU version outperforming the CPU.
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