Pub Date : 2022-09-28DOI: 10.1007/s00371-022-02666-0
Tatsuyoshi Amemiya, Chee Siang Leow, Prawit Buayai, Koji Makino, Xiaoyang Mao, H. Nishizaki
{"title":"Appropriate grape color estimation based on metric learning for judging harvest timing","authors":"Tatsuyoshi Amemiya, Chee Siang Leow, Prawit Buayai, Koji Makino, Xiaoyang Mao, H. Nishizaki","doi":"10.1007/s00371-022-02666-0","DOIUrl":"https://doi.org/10.1007/s00371-022-02666-0","url":null,"abstract":"","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73349626","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}
Pub Date : 2022-09-19DOI: 10.1016/S1077-3142(23)00144-3
F. Ragusa, Antonino Furnari, G. Farinella
{"title":"MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain","authors":"F. Ragusa, Antonino Furnari, G. Farinella","doi":"10.1016/S1077-3142(23)00144-3","DOIUrl":"https://doi.org/10.1016/S1077-3142(23)00144-3","url":null,"abstract":"","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81075983","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}
Pub Date : 2022-09-14DOI: 10.48550/arXiv.2209.07271
Muhammad Asif Khan, H. Menouar, R. Hamila
Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has gained significant attention in computer vision. Over the past few years, various deep learning methods have been developed to achieve state-of-the-art performance. The methods evolved over time vary in many aspects such as model architecture, input pipeline, learning paradigm, computational complexity, and accuracy gains etc. In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting. Although few surveys exist on the topic, our survey is most up-to date and different in several aspects. First, it provides a more meaningful categorization of the most significant contributions by model architectures, learning methods (i.e., loss functions), and evaluation methods (i.e., evaluation metrics). We chose prominent and distinct works and excluded similar works. We also sort the well-known crowd counting models by their performance over benchmark datasets. We believe that this survey can be a good resource for novice researchers to understand the progressive developments and contributions over time and the current state-of-the-art.
{"title":"Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives","authors":"Muhammad Asif Khan, H. Menouar, R. Hamila","doi":"10.48550/arXiv.2209.07271","DOIUrl":"https://doi.org/10.48550/arXiv.2209.07271","url":null,"abstract":"Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has gained significant attention in computer vision. Over the past few years, various deep learning methods have been developed to achieve state-of-the-art performance. The methods evolved over time vary in many aspects such as model architecture, input pipeline, learning paradigm, computational complexity, and accuracy gains etc. In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting. Although few surveys exist on the topic, our survey is most up-to date and different in several aspects. First, it provides a more meaningful categorization of the most significant contributions by model architectures, learning methods (i.e., loss functions), and evaluation methods (i.e., evaluation metrics). We chose prominent and distinct works and excluded similar works. We also sort the well-known crowd counting models by their performance over benchmark datasets. We believe that this survey can be a good resource for novice researchers to understand the progressive developments and contributions over time and the current state-of-the-art.","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73221761","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}
Jianhua Yang, Ke Wang, Ruifeng Li, Zhong Qin, P. Perner
{"title":"A novel fast combine-and-conquer object detector based on only one-level feature map","authors":"Jianhua Yang, Ke Wang, Ruifeng Li, Zhong Qin, P. Perner","doi":"10.2139/ssrn.4003831","DOIUrl":"https://doi.org/10.2139/ssrn.4003831","url":null,"abstract":"","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90265176","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}
Sangwoo Ji, N. Park, Dongbin Na, Bin Zhu, Jong Kim
{"title":"Defending against attacks tailored to transfer learning via feature distancing","authors":"Sangwoo Ji, N. Park, Dongbin Na, Bin Zhu, Jong Kim","doi":"10.2139/ssrn.3993063","DOIUrl":"https://doi.org/10.2139/ssrn.3993063","url":null,"abstract":"","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87477450","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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