International Conference on Signal Processing and Machine Learning最新文献_第2页

A Stereo Matching with Reconstruction Network for Low-light Stereo Vision 基于重建网络的低光立体视觉匹配

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372821

Rui Tang, Geng Zhang, Xuebin Liu

引用次数: 2

Deep Neural Network-Based Scale Feature Model for BVI Detection and Principal Component Extraction 基于深度神经网络的BVI检测及主成分提取尺度特征模型

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372813

Lu Wang, Xiaorui Liu, Xiaoqing Hu, Luyang Guan, Ming Bao

引用次数: 0

An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition 基于骨架的动作识别的注意增强循环图卷积网络

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372814

Xiaolu Ding, Kai Yang, Wai Chen

{"title":"An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition","authors":"Xiaolu Ding, Kai Yang, Wai Chen","doi":"10.1145/3372806.3372814","DOIUrl":"https://doi.org/10.1145/3372806.3372814","url":null,"abstract":"Dynamic movements of human skeleton have attracted more and more attention as a robust modality for action recognition. As not all temporal stages and skeleton joints are informative for action recognition, and the irrelevant information often brings noise which can degrade the detection performance, extracting discriminative temporal and spatial features becomes an important task. In this paper, we propose a novel end-to-end attention-enhanced recurrent graph convolutional network (AR-GCN) for skeleton-based action recognition. An attention-enhanced mechanism is employed in AR-GCN to pay different levels of attention to different temporal stages and spatial joints. This approach overcomes the information loss caused by only using keyframes and key joints. In particular, AR-GCN combines the graph convolutional network (GCN) with the bidirectional recurrent neural network (BRNN), which retains the irregular joints expressive power of the original GCN, while promoting its sequential modeling ability by introducing a recurrent network. Experimental results demonstrate the effectiveness of our proposed model on the widely used NTU and Kinetics datasets.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122609953","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

Entrepreneurship and Role of AI 企业家精神和人工智能的作用

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3374910

A. Sharma

{"title":"Entrepreneurship and Role of AI","authors":"A. Sharma","doi":"10.1145/3372806.3374910","DOIUrl":"https://doi.org/10.1145/3372806.3374910","url":null,"abstract":"The focus on promotion of entrepreneurial activities has been always crucial for economic development of the successful nation. Entrepreneurs are the leaders who innovate and invent ideas that give stimulus to economic growth activities. In the modern era, entrepreneurship is a key determinant of sustainable growth. Literature explains different types of entrepreneurs that are dominant in explaining the economic growth. This study investigates several inhibitors of entrepreneurship in the perpetual economy of India. Study further explores the different motivators of entrepreneurship and examines the impact of those entrepreneurial motivators on economic growth and employment. A focus group interview was conducted with entrepreneurs in 2017. Now a days advancement of technology and Artificial Intelligence (AI) has touched every sphere of our life. This paper tries to focus on the impact made by AI in entrepreneurial activities. In general, factors that enrich entrepreneurship include encouraging social entrepreneurship, improving institutional environment and supports from international organisations. For growth of the country practical implications has been identified, such as improving institutional development, creating supportive business environment with e-commerce, and promoting social entrepreneurship, security.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122626365","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: 1

Discrete Sidelobe Clutter Determination Method Based on Filtering Response Loss 基于滤波响应损失的离散旁瓣杂波确定方法

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372812

He Wen, Chongdi Duan, Weiwei Wang, Yu Li

引用次数: 0

A Small-Footprint End-to-End KWS System in Low Resources 低资源下的小占用端到端KWS系统

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372822

Gui-Xin Shi, Weiqiang Zhang, Hao Wu, Yao Liu

引用次数: 0

Multi-Task Learning Based End-to-End Speaker Recognition 基于端到端说话人识别的多任务学习

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372818

Yuxuan Pan, Weiqiang Zhang

{"title":"Multi-Task Learning Based End-to-End Speaker Recognition","authors":"Yuxuan Pan, Weiqiang Zhang","doi":"10.1145/3372806.3372818","DOIUrl":"https://doi.org/10.1145/3372806.3372818","url":null,"abstract":"Recently, there has been an increasing interest in end-to-end speaker recognition that directly take raw speech waveform as input without any hand-crafted features such as FBANK and MFCC. SincNet is a recently developed novel convolutional neural network (CNN) architecture in which the filters in the first convolutional layer are set to band-pass filters (sinc functions). Experiments show that SincNet achieves a significant decrease in frame error rate (FER) than traditional CNNs and DNNs.\u0000 In this paper we demonstrate how to improve the performance of SincNet using Multi-Task learning (MTL). In the proposed Sinc- Net architecture, besides the main task (speaker recognition), a phoneme recognition task is employed as an auxiliary task. The network uses sinc layers and convolutional layers as shared layers to improve the extensiveness of the network, and the outputs of shared layers are fed into two different sets of full-connected layers for classification. Our experiments, conducted on TIMIT corpora, show that the proposed architecture SincNet-MTL performs better than standard SincNet architecture in both classification error rates (CER) and convergence rate.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128011521","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: 1

Implement AI Service into VR Training 将AI服务融入VR培训

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3374909

J. Suttor, Julian Marin, Evan Verbus, Meng Su

引用次数: 3

A Vision-based Human Action Recognition System for Moving Cameras Through Deep Learning 一种基于视觉的深度学习移动摄像机动作识别系统

International Conference on Signal Processing and Machine Learning Pub Date : 2019-11-27 DOI: 10.1145/3372806.3372815

Ming-Jen Chang, Jih-Tang Hsieh, C. Fang, Sei-Wang Chen

{"title":"A Vision-based Human Action Recognition System for Moving Cameras Through Deep Learning","authors":"Ming-Jen Chang, Jih-Tang Hsieh, C. Fang, Sei-Wang Chen","doi":"10.1145/3372806.3372815","DOIUrl":"https://doi.org/10.1145/3372806.3372815","url":null,"abstract":"This study presents a vision-based human action recognition system using a deep learning technique. The system can recognize human actions successfully when the camera of a robot is moving toward the target person from various directions. Therefore, the proposed method is useful for the vision system of indoor mobile robots. \u0000 The system uses three types of information to recognize human actions, namely, information from color videos, optical flow videos, and depth videos. First, Kinect 2.0 captures color videos and depth videos simultaneously using its RGB camera and depth sensor. Second, the histogram of oriented gradient features is extracted from the color videos, and a support vector machine is used to detect the human region. Based on the detected human region, the frames of the color video are cropped and the corresponding frames of the optical flow video are obtained using the Farnebäck method (https://docs.opencv=.org/3.4/d4/dee/ tutorial_optical_flow.html). The number of frames of these videos is then unified using a frame sampling technique. Subsequently, these three types of videos are input into three modified 3D convolutional neural networks (3D CNNs) separately. The modified 3D CNNs can extract the spatiotemporal features of human actions and recognize them. Finally, these recognition results are integrated to output the final recognition result of human actions. \u0000 The proposed system can recognize 13 types of human actions, namely, drink (sit), drink (stand), eat (sit), eat (stand), read, sit down, stand up, use a computer, walk (horizontal), walk (straight), play with a phone/tablet, walk away from each other, and walk toward each other. The average human action recognition rate of 369 test human action videos was 96.4%, indicating that the proposed system is robust and efficient.","PeriodicalId":340004,"journal":{"name":"International Conference on Signal Processing and Machine Learning","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124648411","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: 11

Deep Activation Feature Maps for Visual Object Tracking 用于视觉对象跟踪的深度激活特征映射

International Conference on Signal Processing and Machine Learning Pub Date : 2018-11-28 DOI: 10.1145/3297067.3297088

Yang Li, Zhuang Miao, Jiabao Wang

引用次数: 0