Recent methods of multi-person pose estimation focus on different aspects to increase the accuracy of keypoints localization. Although fusing the multi-scale feature maps to improve the recognition accuracy has achieved great results, there still have some space to promote. In this paper, we present two novel modules to enhance the multi-scale feature and increase the range of receptive fields by constructing hierarchical residual-like connections. First, the channel shuffle unit and Res2 block are combined to fuse the different level of features in pyramid feature maps, which prompts feature information communication. Second, a new residual block is built to fuse both spatial and channel-wise information within local receptive fields at each layer, and the residual block used in original basic network structure is replaced. The experiment have been evaluated on the COCO keypoint benchmark, which shows that our approach achieves better results than the other state-of-the-arts.
{"title":"Multi-Person Pose Estimation Based on Hierarchical Residual-Like Connections","authors":"Yebo Shen, Xuemei Jiang, Jiwei Hu, P. Lou","doi":"10.1145/3439133.3439134","DOIUrl":"https://doi.org/10.1145/3439133.3439134","url":null,"abstract":"Recent methods of multi-person pose estimation focus on different aspects to increase the accuracy of keypoints localization. Although fusing the multi-scale feature maps to improve the recognition accuracy has achieved great results, there still have some space to promote. In this paper, we present two novel modules to enhance the multi-scale feature and increase the range of receptive fields by constructing hierarchical residual-like connections. First, the channel shuffle unit and Res2 block are combined to fuse the different level of features in pyramid feature maps, which prompts feature information communication. Second, a new residual block is built to fuse both spatial and channel-wise information within local receptive fields at each layer, and the residual block used in original basic network structure is replaced. The experiment have been evaluated on the COCO keypoint benchmark, which shows that our approach achieves better results than the other state-of-the-arts.","PeriodicalId":291985,"journal":{"name":"2020 4th International Conference on Artificial Intelligence and Virtual Reality","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116147568","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}
Boris Velichkov, K. Ivanova, Valeri Hristov, I. Borisov, Alexander Peychev, Ivan Koychev, S. Boytcheva
Medical data for patients is sensitive personal information and therefore to be used in the original form is unacceptable. On the other hand, in order to be able to do various studies and analysis, we need such data. In many cases, such data even anonymized, by removing the personal identifiers, which are not suitable to be shared. Therefore we decided to create a corpus of synthetic statuses of patients that GPs place when performing a general examination. Each status consists of several sentences, each sentence describing the condition of an organ, system or part of the patient's body. We divided the status into its constituent sentences and then each sentence was classified based on the organ it refers to. We build a gold standard of manually classified sentences into list of human body organs and systems. Then we use it to train a neural network classifier of sentences that reaches almost 99% accuracy. Finally, from the all classified sentences we generate synthetic statuses, composed according to statistics in the available real statuses and medical domain constrains. The proposed approach can be easily adapted to other languages.
{"title":"AI-driven Approach for Automatic Synthetic Patient Status Corpus Generation","authors":"Boris Velichkov, K. Ivanova, Valeri Hristov, I. Borisov, Alexander Peychev, Ivan Koychev, S. Boytcheva","doi":"10.1145/3439133.3439141","DOIUrl":"https://doi.org/10.1145/3439133.3439141","url":null,"abstract":"Medical data for patients is sensitive personal information and therefore to be used in the original form is unacceptable. On the other hand, in order to be able to do various studies and analysis, we need such data. In many cases, such data even anonymized, by removing the personal identifiers, which are not suitable to be shared. Therefore we decided to create a corpus of synthetic statuses of patients that GPs place when performing a general examination. Each status consists of several sentences, each sentence describing the condition of an organ, system or part of the patient's body. We divided the status into its constituent sentences and then each sentence was classified based on the organ it refers to. We build a gold standard of manually classified sentences into list of human body organs and systems. Then we use it to train a neural network classifier of sentences that reaches almost 99% accuracy. Finally, from the all classified sentences we generate synthetic statuses, composed according to statistics in the available real statuses and medical domain constrains. The proposed approach can be easily adapted to other languages.","PeriodicalId":291985,"journal":{"name":"2020 4th International Conference on Artificial Intelligence and Virtual Reality","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121944881","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}
{"title":"2020 4th International Conference on Artificial Intelligence and Virtual Reality","authors":"","doi":"10.1145/3439133","DOIUrl":"https://doi.org/10.1145/3439133","url":null,"abstract":"","PeriodicalId":291985,"journal":{"name":"2020 4th International Conference on Artificial Intelligence and Virtual Reality","volume":"3 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133067405","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}
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