The Experts below are selected from a list of 1170 Experts worldwide ranked by ideXlab platform

Chunli Wang - One of the best experts on this subject based on the ideXlab platform.

  • ICPR (1) - A Verification Method for Viewpoint Invariant Sign Language Recognition
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Qi Wang, Xilin Chen, Chunli Wang
    Abstract:

    Viewpoint Variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major Variances between two video sequences of the same sign: Performance Variance and viewpoint Variance. For small Performance Variance, DTW can help us eliminate it. When there is only viewpoint Variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks.

  • A Verification Method for Viewpoint Invariant Sign Language Recognition
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Qi Wang, Xilin Chen, Chunli Wang
    Abstract:

    Viewpoint Variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major Variances between two video sequences of the same sign: Performance Variance and viewpoint Variance. For small Performance Variance, DTW can help us eliminate it. When there is only viewpoint Variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks

Wenguang Chen - One of the best experts on this subject based on the ideXlab platform.

  • vs ensor leveraging fixed workload snippets of programs for Performance Variance detection
    ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018
    Co-Authors: Xiongchao Tang, Jidong Zhai, Xuehai Qian, Bingsheng He, Wenguang Chen
    Abstract:

    Performance Variance becomes increasingly challenging on current large-scale HPC systems. Even using a fixed number of computing nodes, the execution time of several runs can vary significantly. Many parallel programs executing on supercomputers suffer from such Variance. Performance Variance not only causes unpredictable Performance requirement violations, but also makes it unintuitive to understand the program behavior. Despite prior efforts, efficient on-line detection of Performance Variance remains an open problem. In this paper, we propose vS ensor , a novel approach for light-weight and on-line Performance Variance detection. The key insight is that, instead of solely relying on an external detector, the source code of a program itself could reveal the runtime Performance characteristics. Specifically, many parallel programs contain code snippets that are executed repeatedly with an invariant quantity of work. Based on this observation, we use compiler techniques to automatically identify these fixed-workload snippets and use them as Performance v ariance sensor s (v-sensors) that enable effective detection. We evaluate vS ensor with a variety of parallel programs on the Tianhe-2 system. Results show that vS ensor can effectively detect Performance Variance on HPC systems. The Performance overhead is smaller than 4% with up to 16,384 processes. In particular, with vS ensor , we found a bad node with slow memory that slowed a program's Performance by 21%. As a showcase, we also detected a severe network Performance problem that caused a 3.37X slowdown for an HPC kernel program on the Tianhe-2 system.

  • PPOPP - vS ensor : leveraging fixed-workload snippets of programs for Performance Variance detection
    Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018
    Co-Authors: Xiongchao Tang, Jidong Zhai, Xuehai Qian, Bingsheng He, Wenguang Chen
    Abstract:

    Performance Variance becomes increasingly challenging on current large-scale HPC systems. Even using a fixed number of computing nodes, the execution time of several runs can vary significantly. Many parallel programs executing on supercomputers suffer from such Variance. Performance Variance not only causes unpredictable Performance requirement violations, but also makes it unintuitive to understand the program behavior. Despite prior efforts, efficient on-line detection of Performance Variance remains an open problem. In this paper, we propose vS ensor , a novel approach for light-weight and on-line Performance Variance detection. The key insight is that, instead of solely relying on an external detector, the source code of a program itself could reveal the runtime Performance characteristics. Specifically, many parallel programs contain code snippets that are executed repeatedly with an invariant quantity of work. Based on this observation, we use compiler techniques to automatically identify these fixed-workload snippets and use them as Performance v ariance sensor s (v-sensors) that enable effective detection. We evaluate vS ensor with a variety of parallel programs on the Tianhe-2 system. Results show that vS ensor can effectively detect Performance Variance on HPC systems. The Performance overhead is smaller than 4% with up to 16,384 processes. In particular, with vS ensor , we found a bad node with slow memory that slowed a program's Performance by 21%. As a showcase, we also detected a severe network Performance problem that caused a 3.37X slowdown for an HPC kernel program on the Tianhe-2 system.

Qi Wang - One of the best experts on this subject based on the ideXlab platform.

  • ICPR (1) - A Verification Method for Viewpoint Invariant Sign Language Recognition
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Qi Wang, Xilin Chen, Chunli Wang
    Abstract:

    Viewpoint Variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major Variances between two video sequences of the same sign: Performance Variance and viewpoint Variance. For small Performance Variance, DTW can help us eliminate it. When there is only viewpoint Variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks.

  • A Verification Method for Viewpoint Invariant Sign Language Recognition
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Qi Wang, Xilin Chen, Chunli Wang
    Abstract:

    Viewpoint Variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major Variances between two video sequences of the same sign: Performance Variance and viewpoint Variance. For small Performance Variance, DTW can help us eliminate it. When there is only viewpoint Variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks

Xiongchao Tang - One of the best experts on this subject based on the ideXlab platform.

  • vs ensor leveraging fixed workload snippets of programs for Performance Variance detection
    ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018
    Co-Authors: Xiongchao Tang, Jidong Zhai, Xuehai Qian, Bingsheng He, Wenguang Chen
    Abstract:

    Performance Variance becomes increasingly challenging on current large-scale HPC systems. Even using a fixed number of computing nodes, the execution time of several runs can vary significantly. Many parallel programs executing on supercomputers suffer from such Variance. Performance Variance not only causes unpredictable Performance requirement violations, but also makes it unintuitive to understand the program behavior. Despite prior efforts, efficient on-line detection of Performance Variance remains an open problem. In this paper, we propose vS ensor , a novel approach for light-weight and on-line Performance Variance detection. The key insight is that, instead of solely relying on an external detector, the source code of a program itself could reveal the runtime Performance characteristics. Specifically, many parallel programs contain code snippets that are executed repeatedly with an invariant quantity of work. Based on this observation, we use compiler techniques to automatically identify these fixed-workload snippets and use them as Performance v ariance sensor s (v-sensors) that enable effective detection. We evaluate vS ensor with a variety of parallel programs on the Tianhe-2 system. Results show that vS ensor can effectively detect Performance Variance on HPC systems. The Performance overhead is smaller than 4% with up to 16,384 processes. In particular, with vS ensor , we found a bad node with slow memory that slowed a program's Performance by 21%. As a showcase, we also detected a severe network Performance problem that caused a 3.37X slowdown for an HPC kernel program on the Tianhe-2 system.

  • PPOPP - vS ensor : leveraging fixed-workload snippets of programs for Performance Variance detection
    Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018
    Co-Authors: Xiongchao Tang, Jidong Zhai, Xuehai Qian, Bingsheng He, Wenguang Chen
    Abstract:

    Performance Variance becomes increasingly challenging on current large-scale HPC systems. Even using a fixed number of computing nodes, the execution time of several runs can vary significantly. Many parallel programs executing on supercomputers suffer from such Variance. Performance Variance not only causes unpredictable Performance requirement violations, but also makes it unintuitive to understand the program behavior. Despite prior efforts, efficient on-line detection of Performance Variance remains an open problem. In this paper, we propose vS ensor , a novel approach for light-weight and on-line Performance Variance detection. The key insight is that, instead of solely relying on an external detector, the source code of a program itself could reveal the runtime Performance characteristics. Specifically, many parallel programs contain code snippets that are executed repeatedly with an invariant quantity of work. Based on this observation, we use compiler techniques to automatically identify these fixed-workload snippets and use them as Performance v ariance sensor s (v-sensors) that enable effective detection. We evaluate vS ensor with a variety of parallel programs on the Tianhe-2 system. Results show that vS ensor can effectively detect Performance Variance on HPC systems. The Performance overhead is smaller than 4% with up to 16,384 processes. In particular, with vS ensor , we found a bad node with slow memory that slowed a program's Performance by 21%. As a showcase, we also detected a severe network Performance problem that caused a 3.37X slowdown for an HPC kernel program on the Tianhe-2 system.

Xilin Chen - One of the best experts on this subject based on the ideXlab platform.

  • ICPR (1) - A Verification Method for Viewpoint Invariant Sign Language Recognition
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Qi Wang, Xilin Chen, Chunli Wang
    Abstract:

    Viewpoint Variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major Variances between two video sequences of the same sign: Performance Variance and viewpoint Variance. For small Performance Variance, DTW can help us eliminate it. When there is only viewpoint Variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks.

  • A Verification Method for Viewpoint Invariant Sign Language Recognition
    18th International Conference on Pattern Recognition (ICPR'06), 2006
    Co-Authors: Qi Wang, Xilin Chen, Chunli Wang
    Abstract:

    Viewpoint Variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major Variances between two video sequences of the same sign: Performance Variance and viewpoint Variance. For small Performance Variance, DTW can help us eliminate it. When there is only viewpoint Variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks