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Attentional Residual Dense Factorized Network for Real-Time Semantic Segmentation 28th

Attentional Residual Dense Factorized Network for Real-Time ...

引用

28th International conference on Artificial Neural Networks (ICANN)

作者： Yang, Lulu Lan, Long Zhang, Xiang Huang, Xuhui Luo, Zhigang NUDT Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China NUDT Coll Comp Changsha 410073 Hunan Peoples R China NUDT State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China NUDT Dept Comp Sci Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783030305086;9783030305079

Semantic segmentation is a pixel-level image dense labeling task and plays a core role in autonomous driving. In this regard, how to balance between precision and speed is a frequently-studied issue. In this paper, we propose an alternative attentional residual dense factorized network (AttRDFNet) to address this issue. Specifically, we design a residual dense factorized convolution block (RDFB), which reaps the benefits of low-level and high-level layer-wise features through dense connection to boost segmentation precision whilst enjoying efficient computation by factorizing large convolution kernel into the product of two smaller kernels. This reduces computational burdens and makes real time become possible. To further leverage layer-wise features, we explore the graininess-aware channel and spatial attention modules to model different levels of salient features of interest. As a result, AttRDFNet can run with the inputs of the resolution 512 x 1024 at the speed of 55.6 frames per second on a single Titan X GPU with solid 68.5% Mean IOU on the test set of Cityscapes. Experiments on the Cityscapes dataset show that AttRDFNet has real-time inference whilst achieving competitive precision against well-behaved counterparts.

关键词： Real-time semantic segmentation Attention module Residual dense factorized convolution block

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GPU Optimization of Biological Macromolecule Multi-tilt Electron Tomography Reconstruction Algorithm 1

引用

19th International conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Fu, Zi-Ang Wan, Xiaohua Zhang, Fa Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Three-dimensional (3D) reconstruction in cryo-electron tomography (cryo-ET) plays an important role in studying in situ biological macromolecular structures at the nanometer level. Owing to limited tilt angle, 3D reconstruction of cryo-ET always suffers from a "missing wedge" problem which causes severe accuracy degradation. Multi-tilt reconstruction is an effective method to reduce artifacts and suppress the effect of the missing wedge. As the number of tilt series increases, large size data causes high computation and huge memory overhead. Limited by the memory, multi-tilt reconstruction cannot be performed in parallel on GPUs, especially when the image size reaches 1 K, 2 K, or even larger. To optimize large-scale multi-tilt reconstruction of cryo-ET, we propose a newGPU-based large-scale multi-tilt tomographic reconstruction algorithm (GMSIRT). Furthermore, we design a two-level data partition strategy in GM-SIRT to greatly reduce the memory required in the whole reconstructing process. Experimental results show that the performance of the GM-SIRT algorithm has been significantly improved compared with DM-SIRT, the distributed multi-tilt reconstruction algorithm on the CPU cluster. The acceleration ratio is over 300%, and the memory requirement only decreases to one-third of DM-SIRT when the image size reaches 2 K.

关键词： Graphics processing unit

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Evaluation of parallel Window-Join Algorithms for Reduction of processing Delay

Evaluation of Parallel Window-Join Algorithms for Reduction ...

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International conference on P2P, parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Yousuke Watanabe Haruo Yokota Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan Graduate School of Information Science and Engineering Tokyo Institute of Technology Tokyo Japan

The number of stream-based information sources is increasing, so query processing on streams is getting more important. Window-join is one of typical operations for stream data processing. It uses sliding-windows to keep recent input tuples, and generate the join result from the tuples in the window. To reduce processing cost of window-join, some parallel window-join algorithms have been proposed in the context of distributed environments. Their main objectives are increasing throughput. They do not concern about reducing processing delay, because delay caused network transfer is unavoidable in distributed environments. In our research, we focus on reducing processing delay of window-join by using multi-core environments instead of distributed environments. We propose and evaluate two parallel window-join algorithms for multiple input streams. The first one is parallel Tree-based Join, which makes multiple cores execute multiple join trees with different join orders. Each join tree is optimized to minimize processing delay for tuples from one stream. Another is parallel MJoin, which is an extension of existing MJoin algorithm. parallel MJoin concurrently performs multiple probe processes instead of sequential probe used in original MJoin. This paper also includes experimental evaluation to investigate efficiency of two methods.

关键词： Delays Probes Silicon Tin Instruction sets Engines Multicore processing

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parallel image classification using multiscale Markov random fields

Parallel image classification using multiscale Markov random...

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1993 IEEE International conference on Acoustics, Speech, and Signal processing

作者： Z. Kato M. Berthod J. Zerubia INRIA Sophia-Antipolis France

The application of massively parallel multiscale relaxation algorithms to image classification is considered. First, a classical multiscale model applied to supervised image classification is presented. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, a scheme which introduces a local interaction between two neighbor grids in the label pyramid is proposed. This is a way to incorporate cliques, with far-apart sites for a reasonable price. Finally, results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models are presented.< >

关键词： image classification Markov random fields Grid computing Petroleum Relaxation methods image resolution Tin Mesh generation

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8th International conference on Similarity Search and Applications, SISAP 2015

8th International Conference on Similarity Search and Applic...

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8th International conference on Similarity Search and Applications, SISAP 2015

ISBN: (纸本)9783319250861

The proceedings contain 41 papers. The special focus in this conference is on Improving Similarity Search methods, Techniques, Metrics and Evaluation. The topics include: Approximate furthest neighbor in high dimensions;flexible aggregate similarity search in high-dimensional data sets;similarity joins and beyond;diversity in similarity joins;succinct spaghetti;improving metric access methods with bucket files;faster dual-tree traversal for nearest neighbor search;optimizing the distance computation order of multi-feature similarity search indexing;dynamic permutation based index for proximity searching;finding near neighbors through local search;when similarity measures lie;an empirical evaluation of intrinsic dimension estimators;a belief framework for similarity evaluation of textual or structured data;similarity of attributed generalized tree structures;evaluating multilayer multimedia exploration;semantic similarity between images;vector-based similarity measurements for historical figures;efficient approximate 3-dimensional point set matching using root-mean-square deviation score;face image retrieval revisited;semiautomatic learning of 3d objects from video streams;banknote recognition as a CBIR problem;efficient image search with neural net features;textual similarity for word sequences;an effective representation of motion capture data for similarity search;brute-force k-nearest neighbors search on the GPU;regrouping metric-space search index for search engine size adaptation and improving parallel processing of matrix-based similarity measures on modern GPUs.

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Topology-aware deployment of scientific applications in cloud computing

Topology-aware deployment of scientific applications in clou...

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2012 IEEE 5th International conference on Cloud Computing, CLOUD 2012

作者： Fan, Pei Chen, Zhenbang Wang, Ji Zheng, Zibin Lyu, Michael R. National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Dept. of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9780769547558

Nowadays, more and more scientific applications are moving to cloud computing. The optimal deployment of scientific applications is critical for providing good services to users. Scientific applications are usually topology-aware applications. Therefore, considering the topology of a scientific application during the development will benefit the performance of the application. However, it is challenging to automatically discover and make use of the communication pattern of a scientific application while deploying the application on cloud. To attack this challenge, in this paper, we propose a framework to discover the communication topology of a scientific application by pre-execution and multi-scale graph clustering, based on which the deployment can be optimized. Comprehensive experiments are conducted by employing a well-known MPI benchmark and comparing the performance of our method with those of other methods. The experimental results show the effectiveness of our topology-aware deployment method. © 2012 IEEE.

关键词： Cloud computing

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Graph-based vectorization method for line patterns

Graph-based vectorization method for line patterns

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： S. Suzuki NTT Human Interface Laboratories Yokosuka Japan

A vectorization method for line patterns is proposed which converts digital binary images into line segment vectors. The vector data is more compact and more natural than that obtained by conventional methods using thinning operations. The proposed method consisted of four steps. First, thinning of an input binary image is performed. Then a medical line image obtained by the thinning operation is transformed into a graph, in which pixels on the medial line correspond to nodes and neighboring nodes are connected by edges. Next, extra edges unnecessary for preserving the topology of the medial line image are deleted. The deletion can be implemented as an iterative parallel operation. Finally, the graph is simplified by line approximation. Every step except the line approximation is suitable for parallel processing. The experimental results of applying the proposed method to geographical maps show that the method reduces data volume by about 30%, as compared with conventional methods.< >

关键词： parallel processing image converters Topology Character recognition Humans image segmentation Pixel image processing Shape Laboratories

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A parallel Artificial Immune Model for Optimization

A Parallel Artificial Immune Model for Optimization

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International conference on Computational Intelligence and Security

作者： Yutao Qi Fang Liu Licheng Jiao School of Computer Science And Technology Key Laboratory of Intelligent Perception and Imagee Understanding of Ministry of Education Xidian University Xi'an China Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time.

关键词： Immune system Genetic algorithms Artificial immune systems Competitive intelligence Master-slave Laboratories Computer science education Poles and towers Vaccines parallel processing

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A Regional Density Distribution based Wide Dynamic Range algorithm for Infrared Camera Systems 11

A Regional Density Distribution based Wide Dynamic Range alg...

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conference on Electro-Optical and Infrared Systems - Technology and Applications XI

作者： Park, Gyuhee Kim, Yongsung Joung, Shichang Shin, Sanghoon Emerging Technol R&D Ctr Video Technol Lab Sktelecom South Korea

ISBN: (纸本)9781628413120

Forward Looking InfraRed (FLIR) imaging system has been widely used for both military and civilian purposes. Military applications include target acquisition and tracking, night vision system. Civilian applications include thermal efficiency analysis, short-ranged wireless communication, weather forecasting and other various applications. The dynamic range of FLIR imaging system is larger than one of commercial display. Generally, auto gain controlling and contrast enhancement algorithm are applied to FLIR imaging system. In IR imaging system, histogram equalization and plateau equalization is generally used for contrast enhancement. However, they have no solution about the excessive enhancing when luminance histogram has been distributed in specific narrow region. In this paper, we proposed a Regional Density Distribution based Wide Dynamic Range algorithm for Infrared Camera Systems. Depending on the way of implementation, the result of WDR is quite different. Our approach is single frame type WDR algorithm for enhancing the contrast of both dark and white detail without loss of bins of histogram with real-time processing. The significant change in luminance caused by conventional contrast enhancement methods may introduce luminance saturation and failure in object tracking. Proposed method guarantees both the effective enhancing in contrast and successive object tracking. Moreover, since proposed method does not using multiple images on WDR, computation complexity might be significantly reduced in software / hardware implementation. The experimental results show that proposed method has better performance compared with conventional Contrast enhancement methods.

关键词： Wide Dynamic Range Contrast Enhancement image processing Histogram Infrared Cameras

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CUTTANA: Scalable Graph Partitioning for Faster distributed Graph Databases and Analytics 51st

CUTTANA: Scalable Graph Partitioning for Faster Distributed ...

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51st International conference on Very Large Data Bases, VLDB 2025

作者： Hajidehi, Milad Rezaei Sridhar, Sraavan Seltzer, Margo University of British Columbia Vancouver Canada

Graph partitioning plays a pivotal role in various distributed graph processing applications, including graph analytics, graph neural network training, and distributed graph databases. A "good" graph partitioner reduces workload execution time, worker imbalance, and network overhead. Graphs that require distributed settings are often too large to fit in the main memory of a single machine. This challenge renders traditional in-memory graph partitioners infeasible, leading to the emergence of streaming solutions. Streaming partitioners produce lower-quality partitions, because they work from partial information and must make premature decisions before they have a complete view of a vertex’s neighborhood. We introduce CUTTANA, a streaming graph partitioner that partitions massive graphs (Web/Twitter scale) with superior quality compared to existing streaming solutions. CUTTANA uses a novel buffering technique that prevents the premature assignment of vertices to partitions and a scalable coarsening and refinement technique that enables a complete graph view, improving the intermediate assignment made by a streaming partitioner. We implemented a parallel version for CUTTANA that offers nearly the same partitioning latency as existing streaming partitioners. Our experimental analysis shows that CUTTANA consistently yields better partitioning quality than state-of-the-art streaming vertex partitioners in terms of both edge-cut and communication volume metrics. We also evaluate the workload latencies that result from using CUTTANA and other partitioners in distributed graph analytics and databases. CUTTANA outperforms the other methods in most scenarios (algorithms, datasets). In analytics applications, CUTTANA improves runtime performance by up to 59% compared to various streaming partitioners (i.e., HDRF, Fennel, Ginger, HeiStream). In graph database tasks, CUTTANA results in higher query throughput by up to 23%, without hurting tail latency. © 2024, VLDB Endowment. All

关键词： Graph Databases

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