IEICE Transactions on Information and Systems最新文献_第6页

Regressive Gaussian Process Latent Variable Model for Few-Frame Human Motion Prediction 基于回归高斯过程潜变量模型的少帧人体运动预测

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2023pcp0001

Xin JIN, Jia GUO

{"title":"Regressive Gaussian Process Latent Variable Model for Few-Frame Human Motion Prediction","authors":"Xin JIN, Jia GUO","doi":"10.1587/transinf.2023pcp0001","DOIUrl":"https://doi.org/10.1587/transinf.2023pcp0001","url":null,"abstract":"Human motion prediction has always been an interesting research topic in computer vision and robotics. It means forecasting human movements in the future conditioning on historical 3-dimensional human skeleton sequences. Existing predicting algorithms usually rely on extensive annotated or non-annotated motion capture data and are non-adaptive. This paper addresses the problem of few-frame human motion prediction, in the spirit of the recent progress on manifold learning. More precisely, our approach is based on the insight that achieving an accurate prediction relies on a sufficiently linear expression in the latent space from a few training data in observation space. To accomplish this, we propose Regressive Gaussian Process Latent Variable Model (RGPLVM) that introduces a novel regressive kernel function for the model training. By doing so, our model produces a linear mapping from the training data space to the latent space, while effectively transforming the prediction of human motion in physical space to the linear regression analysis in the latent space equivalent. The comparison with two learning motion prediction approaches (the state-of-the-art meta learning and the classical LSTM-3LR) demonstrate that our GPLVM significantly improves the prediction performance on various of actions in the small-sample size regime.","PeriodicalId":55002,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Network-Based Post-Processing Filter on V-PCC Attribute Frames 基于神经网络的V-PCC属性帧后处理滤波

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2023pcl0002

Keiichiro TAKADA, Yasuaki TOKUMO, Tomohiro IKAI, Takeshi CHUJOH

引用次数: 0

GPU-Accelerated Estimation and Targeted Reduction of Peak IR-Drop during Scan Chain Shifting 扫描链移位过程中峰值红外降的gpu加速估计和目标降低

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2023edp7011

Shiling SHI, Stefan HOLST, Xiaoqing WEN

{"title":"GPU-Accelerated Estimation and Targeted Reduction of Peak IR-Drop during Scan Chain Shifting","authors":"Shiling SHI, Stefan HOLST, Xiaoqing WEN","doi":"10.1587/transinf.2023edp7011","DOIUrl":"https://doi.org/10.1587/transinf.2023edp7011","url":null,"abstract":"High power dissipation during scan test often causes undue yield loss, especially for low-power circuits. One major reason is that the resulting IR-drop in shift mode may corrupt test data. A common approach to solving this problem is partial-shift, in which multiple scan chains are formed and only one group of scan chains is shifted at a time. However, existing partial-shift based methods suffer from two major problems: (1) their IR-drop estimation is not accurate enough or computationally too expensive to be done for each shift cycle; (2) partial-shift is hence applied to all shift cycles, resulting in long test time. This paper addresses these two problems with a novel IR-drop-aware scan shift method, featuring: (1) Cycle-based IR-Drop Estimation (CIDE) supported by a GPU-accelerated dynamic power simulator to quickly find potential shift cycles with excessive peak IR-drop; (2) a scan shift scheduling method that generates a scan chain grouping targeted for each considered shift cycle to reduce the impact on test time. Experiments on ITC'99 benchmark circuits show that: (1) the CIDE is computationally feasible; (2) the proposed scan shift schedule can achieve a global peak IR-drop reduction of up to 47%. Its scheduling efficiency is 58.4% higher than that of an existing typical method on average, which means our method has less test time.","PeriodicalId":55002,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias 基于可学习赤道偏差的全向显著性图多尺度估计

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2023edp7055

Takao YAMANAKA, Tatsuya SUZUKI, Taiki NOBUTSUNE, Chenjunlin WU

{"title":"Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias","authors":"Takao YAMANAKA, Tatsuya SUZUKI, Taiki NOBUTSUNE, Chenjunlin WU","doi":"10.1587/transinf.2023edp7055","DOIUrl":"https://doi.org/10.1587/transinf.2023edp7055","url":null,"abstract":"Omni-directional images have been used in wide range of applications including virtual/augmented realities, self-driving cars, robotics simulators, and surveillance systems. For these applications, it would be useful to estimate saliency maps representing probability distributions of gazing points with a head-mounted display, to detect important regions in the omni-directional images. This paper proposes a novel saliency-map estimation model for the omni-directional images by extracting overlapping 2-dimensional (2D) plane images from omni-directional images at various directions and angles of view. While 2D saliency maps tend to have high probability at the center of images (center bias), the high-probability region appears at horizontal directions in omni-directional saliency maps when a head-mounted display is used (equator bias). Therefore, the 2D saliency model with a center-bias layer was fine-tuned with an omni-directional dataset by replacing the center-bias layer to an equator-bias layer conditioned on the elevation angle for the extraction of the 2D plane image. The limited availability of omni-directional images in saliency datasets can be compensated by using the well-established 2D saliency model pretrained by a large number of training images with the ground truth of 2D saliency maps. In addition, this paper proposes a multi-scale estimation method by extracting 2D images in multiple angles of view to detect objects of various sizes with variable receptive fields. The saliency maps estimated from the multiple angles of view were integrated by using pixel-wise attention weights calculated in an integration layer for weighting the optimal scale to each object. The proposed method was evaluated using a publicly available dataset with evaluation metrics for omni-directional saliency maps. It was confirmed that the accuracy of the saliency maps was improved by the proposed method.","PeriodicalId":55002,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralized Incentive Scheme for Peer-to-Peer Video Streaming using Solana Blockchain 使用Solana区块链的点对点视频流的分散激励方案

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2023edp7027

Yunqi MA, Satoshi FUJITA

引用次数: 0

Feedback Node Sets in Pancake Graphs and Burnt Pancake Graphs 煎饼图和烧焦煎饼图中的反馈节点集

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2022edp7211

Sinyu JUNG, Keiichi KANEKO

引用次数: 0

Important Notice of the Cancellation of Special Section on Formal Approaches 关于取消正式申请特别部分的重要通知

4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-10-01 DOI: 10.1587/transinf.2022fop0000

引用次数: 0

Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance 基于深度学习的视频监控可重构行人检测系统

IF 0.7 4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-09-01 DOI: 10.1587/transinf.2019edl8132

M. K. Jeevarajan, P. N. Kumar

引用次数: 0

Framework of Measuring Engagement with Access Logs Under Tracking Prevention for Affiliate Services 关联服务跟踪预防下访问日志参与度度量框架

IF 0.7 4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-09-01 DOI: 10.1587/transinf.2022ofp0001

Motoi Iwashita, Hirotaka Sugita

引用次数: 0

Discriminative Question Answering via Cascade Prompt Learning and Sentence Level Attention Mechanism 基于级联提示学习和句子级注意机制的辨别性问题回答

IF 0.7 4区计算机科学

IEICE Transactions on Information and Systems Pub Date : 2023-09-01 DOI: 10.1587/transinf.2022edp7225

Xiaoguang Yuan, Chaofan Dai, Zongkai Tian, Xinyu Fan, Yingyi Song, Ze Yu, Peifeng Wang, Wenjun Ke

引用次数: 0