ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第8页

Group Sparsity Based Target Localization for Distributed Sensor Array Networks 基于组稀疏度的分布式传感器阵列网络目标定位

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683867

Qing Shen, Wei Liu, Li Wang, Yin Liu

引用次数: 3

Estimation of Network Processes via Blind Graph Multi-filter Identification 基于盲图多滤波器辨识的网络过程估计

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683844

Yu Zhu, F. J. Garcia, A. Marques, Santiago Segarra

引用次数: 4

An Efficient Algorithm for Hyperspectral Image Clustering 一种高效的高光谱图像聚类算法

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683309

Yushu Pan, Yuchen Jiao, Tiejian Li, Yuantao Gu

引用次数: 2

Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec 使用元数据辅助扩展的3GPP EVS编解码器的虚拟现实沉浸式音频编码

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683712

D. McGrath, S. Bruhn, H. Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy

引用次数: 3

Nonlinear Acceleration of Constrained Optimization Algorithms 约束优化算法的非线性加速

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682962

Vien V. Mai, M. Johansson

引用次数: 9

Block-randomized Stochastic Proximal Gradient for Constrained Low-rank Tensor Factorization 约束低秩张量分解的块随机随机近端梯度

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682465

Xiao Fu, Cheng Gao, Hoi-To Wai, Kejun Huang

{"title":"Block-randomized Stochastic Proximal Gradient for Constrained Low-rank Tensor Factorization","authors":"Xiao Fu, Cheng Gao, Hoi-To Wai, Kejun Huang","doi":"10.1109/ICASSP.2019.8682465","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682465","url":null,"abstract":"This work focuses on canonical polyadic decomposition (CPD) for large-scale tensors. Many prior works rely on data sparsity to develop scalable CPD algorithms, which are not suitable for handling dense tensor, while dense tensors often arise in applications such as image and video processing. As an alternative, stochastic algorithms utilize data sampling to reduce per-iteration complexity and thus are very scalable, even when handling dense tensors. However, existing stochastic CPD algorithms are facing some challenges. For example, some algorithms are based on randomly sampled tensor entries, and thus each iteration can only updates a small portion of the latent factors. This may result in slow improvement of the estimation accuracy of the latent factors. In addition, the convergence properties of many stochastic CPD algorithms are unclear, perhaps because CPD poses a hard nonconvex problem and is challenging for analysis under stochastic settings. In this work, we propose a stochastic optimization strategy that can effectively circumvent the above challenges. The proposed algorithm updates a whole latent factor at each iteration using sampled fibers of a tensor, which can quickly increase the estimation accuracy. The algorithm is flexible—many commonly used regularizers and constraints can be easily incorporated in the computational framework. The algorithm is also backed by a rigorous convergence theory. Simulations on large-scale dense tensors are employed to showcase the effectiveness of the algorithm.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"7485-7489"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80416538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Surgical Activities Recognition Using Multi-scale Recurrent Networks 基于多尺度递归网络的手术活动识别

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683849

Ilker Gurcan, H. Nguyen

{"title":"Surgical Activities Recognition Using Multi-scale Recurrent Networks","authors":"Ilker Gurcan, H. Nguyen","doi":"10.1109/ICASSP.2019.8683849","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683849","url":null,"abstract":"Recently, surgical activity recognition has been receiving significant attention from the medical imaging community. Existing state-of-the-art approaches employ recurrent neural networks such as long-short term memory networks (LSTMs). However, our experiments show that these networks are not effective in capturing the relationship of features with different temporal scales. Such limitation will lead to sub-optimal recognition performance of surgical activities containing complex motions at multiple time scales. To overcome this shortcoming, our paper proposes a multi-scale recurrent neural network (MS-RNN) that combines the strength of both wavelet scattering operations and LSTM. We validate the effectiveness of the proposed network using both real and synthetic datasets. Our experimental results show that MS-RNN outperforms state-of-the-art methods in surgical activity recognition by a significant margin. On a synthetic dataset, the proposed network achieves more than 90% classification accuracy while LSTM’s accuracy is around chance level. Experiments on real surgical activity dataset shows a significant improvement of recognition accuracy over the current state of the art (90.2% versus 83.3%).","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"13 1","pages":"2887-2891"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82478817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Multi-step Self-attention Network for Cross-modal Retrieval Based on a Limited Text Space 基于有限文本空间的跨模态检索多步自关注网络

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682424

Zheng Yu, Wenmin Wang, Ge Li

引用次数: 0

Reflection Symmetry Detection by Embedding Symmetry in a Graph 在图中嵌入对称的反射对称检测

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682412

R. Nagar, S. Raman

引用次数: 4

Convex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms 集隶属仿射投影算法约束向量的凸组合

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682305

T. Ferreira, W. Martins, Markus V. S. Lima, P. Diniz

引用次数: 3