Pub Date : 2024-04-08DOI: 10.1109/tcds.2024.3386364
Md. Nurul Ahad Tawhid, Siuly Siuly, Kate Wang, Hua Wang
{"title":"GENet: A Generic Neural Network for Detecting Various Neurological Disorders from EEG","authors":"Md. Nurul Ahad Tawhid, Siuly Siuly, Kate Wang, Hua Wang","doi":"10.1109/tcds.2024.3386364","DOIUrl":"https://doi.org/10.1109/tcds.2024.3386364","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1109/TCDS.2024.3373151
{"title":"IEEE Transactions on Cognitive and Developmental Systems Publication Information","authors":"","doi":"10.1109/TCDS.2024.3373151","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3373151","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10491580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1109/TCDS.2024.3372274
Junpei Zhong;Ran Dong;Soichiro Ikuno;Yanan Li;Chenguang Yang
Movements play a critical role in robotic systems, with considerations varying across different robotic systems regarding factors, such as accuracy, speed, energy consumption, and naturalness of movements in various parts of the robotic mechanics. Over the past decades, the robotics community has developed computationally efficient mathematical tools for studying, simulating, and optimizing movements of articulated bodies to address these challenges.
{"title":"Guest Editorial Special Issue on Movement Sciences in Cognitive Systems","authors":"Junpei Zhong;Ran Dong;Soichiro Ikuno;Yanan Li;Chenguang Yang","doi":"10.1109/TCDS.2024.3372274","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3372274","url":null,"abstract":"Movements play a critical role in robotic systems, with considerations varying across different robotic systems regarding factors, such as accuracy, speed, energy consumption, and naturalness of movements in various parts of the robotic mechanics. Over the past decades, the robotics community has developed computationally efficient mathematical tools for studying, simulating, and optimizing movements of articulated bodies to address these challenges.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10491578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1109/TCDS.2024.3373155
{"title":"IEEE Transactions on Cognitive and Developmental Systems Information for Authors","authors":"","doi":"10.1109/TCDS.2024.3373155","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3373155","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10491285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fully supervised semantic segmentation requires detailed annotation of each pixel, which is time-consuming and laborious at the pixel-by-pixel level. To solve this problem, the direction of this article is to perform the semantic segmentation task by using image-level categorical annotation. Existing methods using image level annotation usually use class activation maps (CAMs) to find the location of the target object as the first step. By training a classifier, the presence of objects in the image can be searched effectively. However, CAMs appear that as follows: 1) objects are excessively focused on specific regions, capturing only the most prominent and critical areas and 2) it is easy to misinterpret the frequently occurring background regions, the foreground and background are confused. This article introduces cross language image matching based on out-of-distribution data and convolutional block attention module (CLODA), the concept of double branching in the cross language image matching framework, and adds a convolutional attention module to the attention branch to solve the problem of excess focus on objects in the CAMs. Importing out-of-distribution data on out of distribution branches helps classification networks improve misinterpretation of areas of focus. Optimizing regions of interest for attentional branch learning using cross pseudosupervision on two branches. Experimental results show that the pseudomasks generated by the proposed network can achieve 75.3% in mean Intersection over Union (mIoU) with the pattern analysis, statistical modeling and computational learning visual object classes (PASCAL VOC) 2012 training set. The performance of the segmentation network trained with the pseudomasks is up to 72.3% and 72.1% in mIoU on the validation and testing set of PASCAL VOC 2012.
{"title":"Attention Mechanism and Out-of-Distribution Data on Cross Language Image Matching for Weakly Supervised Semantic Segmentation","authors":"Chi-Chia Sun;Jing-Ming Guo;Chen-Hung Chung;Bo-Yu Chen","doi":"10.1109/TCDS.2024.3382914","DOIUrl":"10.1109/TCDS.2024.3382914","url":null,"abstract":"The fully supervised semantic segmentation requires detailed annotation of each pixel, which is time-consuming and laborious at the pixel-by-pixel level. To solve this problem, the direction of this article is to perform the semantic segmentation task by using image-level categorical annotation. Existing methods using image level annotation usually use class activation maps (CAMs) to find the location of the target object as the first step. By training a classifier, the presence of objects in the image can be searched effectively. However, CAMs appear that as follows: 1) objects are excessively focused on specific regions, capturing only the most prominent and critical areas and 2) it is easy to misinterpret the frequently occurring background regions, the foreground and background are confused. This article introduces cross language image matching based on out-of-distribution data and convolutional block attention module (CLODA), the concept of double branching in the cross language image matching framework, and adds a convolutional attention module to the attention branch to solve the problem of excess focus on objects in the CAMs. Importing out-of-distribution data on out of distribution branches helps classification networks improve misinterpretation of areas of focus. Optimizing regions of interest for attentional branch learning using cross pseudosupervision on two branches. Experimental results show that the pseudomasks generated by the proposed network can achieve 75.3% in mean Intersection over Union (mIoU) with the pattern analysis, statistical modeling and computational learning visual object classes (PASCAL VOC) 2012 training set. The performance of the segmentation network trained with the pseudomasks is up to 72.3% and 72.1% in mIoU on the validation and testing set of PASCAL VOC 2012.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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