11th International Conference of Pattern Recognition Systems (ICPRS 2021)最新文献

Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection 稀疏激光雷达和立体融合(SLS-Fusion)用于深度估计和三维目标检测

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 2021-03-05 DOI: 10.1049/icp.2021.1442

Nguyen Anh Minh Mai, Pierre Duthon, L. Khoudour, Alain Crouzil, S. Velastín

{"title":"Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection","authors":"Nguyen Anh Minh Mai, Pierre Duthon, L. Khoudour, Alain Crouzil, S. Velastín","doi":"10.1049/icp.2021.1442","DOIUrl":"https://doi.org/10.1049/icp.2021.1442","url":null,"abstract":"The ability to accurately detect and localize objects is recognized as being the most important for the perception of selfdriving cars. From 2D to 3D object detection, the most difficult is to determine the distance from the ego-vehicle to objects. Expensive technology like LiDAR can provide a precise and accurate depth information, so most studies have tended to focus on this sensor showing a performance gap between LiDAR-based methods and camera-based methods. Although many authors have investigated how to fuse LiDAR with RGB cameras, as far as we know there are no studies to fuse LiDAR and stereo in a deep neural network for the 3D object detection task. This paper presents SLS-Fusion, a new approach to fuse data from 4-beam LiDAR and a stereo camera via a neural network for depth estimation to achieve better dense depth maps and thereby improves 3D object detection performance. Since 4-beam LiDAR is cheaper than the well-known 64-beam LiDAR, this approach is also classified as a low-cost sensorsbased method. Through evaluation on the KITTI benchmark, it is shown that the proposed method significantly improves depth estimation performance compared to a baseline method. Also when applying it to 3D object detection, a new state of the art on low-cost sensor based method is achieved.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132362652","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: 13

A Comparison of Different Embedding Methods on Session Based Recommendation with Graph Neural Networks 基于会话推荐的不同嵌入方法与图神经网络的比较

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1436

M. Aker, C. E. Yıldız, Y. Yaslan

{"title":"A Comparison of Different Embedding Methods on Session Based Recommendation with Graph Neural Networks","authors":"M. Aker, C. E. Yıldız, Y. Yaslan","doi":"10.1049/icp.2021.1436","DOIUrl":"https://doi.org/10.1049/icp.2021.1436","url":null,"abstract":"Predicting users' next behavior based on their previous actions is one of the most valuable but also difficult task in the e-commerce and e-marketing fields. Recommendation systems that build upon session-based data try to bring a solution to that desire. The ultimate goal of this type of recommendation system is trying to make the best predictions about the succeeding item. Sequential order of the items within a session is also kept in mind in such systems. Recently proposed SR-GNN (Session Based Recommendation with Graph Neural Networks) has benefited from graph theory and proven its adequacy about being the state-of-art session-based recommendation model. Furthermore, there are some parts exist that can improve the overall performance. The current model uses primitive embedding type which is the simplest way of representing the items, attributes, and their relationships between each other. This study brings the SR-GNN recommendation model with di erent types of graph embedding techniques which are widely used in a variety of research areas. Aim of this research is investigating the the e ect of the embedding types to the SR-GNN. The proposed variety of embedding techniques that applied to SR-GNN show similar but slightly worse results compared to the original SR-GNN embedding. The experimental results obtained on two real datasets show that the performance of the SR-GNN model is not a ected by the embedding models and the power of the model comes from the gated graph neural network model.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116393084","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: 0

Regional Delineation Based on A Modularity Maximization Approach 基于模块化最大化方法的区域划分

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1461

Qinghe Liu, Zhicheng Liu, Yinfei Xu, Weiting Xiong, Junyan Yang, Qiao Wang

引用次数: 0

Image-Text Integration Using a Multimodal Fusion Network Module for Movie Genre Classification 基于多模态融合网络模块的图像-文本集成电影类型分类

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1456

Leodécio Braz, Vinicius Teixeira, H. Pedrini, Z. Dias

引用次数: 2

Enforced Isolation Deep Network For Anomaly Detection In Images 用于图像异常检测的强制隔离深度网络

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1441

Demetris Lappas, Vasileios Argyriou, Dimitrios Makris

引用次数: 0

Multiple Object Tracking for Robust Quantitative Analysis of Passenger Motion While Boarding and Alighting a Metropolitan Train 基于多目标跟踪的城铁上下车乘客运动鲁棒定量分析

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1468

José Sebastián Gómez Meza, J. Delpiano, S. Velastín, R. Fernández, Sebastián Seriani Awad

引用次数: 0

Improved Cloud-NARX Estimation Algorithm for Uncertainty Analysis of Air Pollution Prediction 改进的云- narx估计算法用于大气污染预测的不确定性分析

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1440

Y. Gu, B. Li, Q. Meng, P. Shang

引用次数: 1

Emotion recognition using multimodal matchmap fusion and multi-task learning 基于多模态匹配图融合和多任务学习的情绪识别

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1454

Ricardo Pizarro, Juan Bekios-Calfa

{"title":"Emotion recognition using multimodal matchmap fusion and multi-task learning","authors":"Ricardo Pizarro, Juan Bekios-Calfa","doi":"10.1049/icp.2021.1454","DOIUrl":"https://doi.org/10.1049/icp.2021.1454","url":null,"abstract":"Emotion recognition is a complex task due to the great intraclass and inter-class variability that exists implicitly in the problem. From the point of view of the intra-class, an emotion can be expressed by different people, which generates different representations of it. For the inter-class case, there are some kinds of emotions that are alike. Traditionally, the problem has been approached in different ways, highlighting the analysis of images to determine the facial expression of a person to extrapolate it to a type of emotion, also, the use of audio sequences to estimate the emotion of the speaker. The present work seeks to solve this problem using multimodal techniques, multitask and Deep Learning. To help with these problems, the use of a fusion method based on the similarity between audio and video modalities will be investigated and applied to the emotion classification problem. The use of this method allows the use of auxiliary tasks that enhance the learned relationships between the emotions shown in video frames and audio frames belonging to the same emotion label and punish those that are different. The results show that when using the fusion method based on the similarity of modalities together with the use of multiple tasks, the classification is improved by 7% with respect to the classification obtained in the baseline model that uses concatenation of the characteristics of each modality, the experiments are performed on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116993082","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

Labeling Consecutive Search Query Pairs Using Siamese Networks 使用暹罗网络标记连续搜索查询对

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1464

N. Ateş, Y. Yaslan

{"title":"Labeling Consecutive Search Query Pairs Using Siamese Networks","authors":"N. Ateş, Y. Yaslan","doi":"10.1049/icp.2021.1464","DOIUrl":"https://doi.org/10.1049/icp.2021.1464","url":null,"abstract":"As internet users interact with search engines to meet their information needs, a huge amount of search queries are stored. Proper analysis of such query data enhances prediction and understanding of user tasks. User tasks can be used to increase the performance of search engines and recommendations. Query segmentation is an initial step that is commonly performed while analyzing user queries. It determines whether two consecutive query expressions belong to the same sub-task. Any deficits in query segmentation process is likely to affect all other advanced query based steps and activities like task identification and query suggestion. Recently, some researchers focused on application of algorithms including Recurrent Neural Networks (RNN) to seek for the semantics of queries, and attention based neural networks, but such methodologies are not task-specific. In this paper, we propose a Siamese Convolutional Neural Network (CNN) that models input queries into a more task-specific embedding and a decider network that does the labelling. The proposed method is compared with Context Attention Based Long Short Term Memory (CA-LSTM) and Bi-RNN Gated Retified Unit (GRU) models on Webis Search Mission Corpus 2012 (WSMC12) and Cross-Session Task Extraction (CSTE) datasets. The proposed model performs 95%, implying a 1% improvement over the already existing models and an accuracy of 81% on CSTE dataset implying an improvement classification accuracy of 6% over the previous best results.","PeriodicalId":431144,"journal":{"name":"11th International Conference of Pattern Recognition Systems (ICPRS 2021)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130564059","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: 0

Investigating the use of multiple languages for crisp and fuzzy speaker identification 调查使用多种语言进行清晰和模糊的说话人识别

11th International Conference of Pattern Recognition Systems (ICPRS 2021) Pub Date : 1900-01-01 DOI: 10.1049/icp.2021.1431

T. Aguiar de Lima, M. Da Costa-Abreu

引用次数: 0