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2021 17th International Conference on Machine Vision and Applications (MVA)最新文献

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Human-Object Interaction Detection with Missing Objects 缺失对象的人-物交互检测
2021 17th International Conference on Machine Vision and Applications (MVA) Pub Date : 2021-07-25 DOI: 10.23919/MVA51890.2021.9511361
Kaen Kogashi, Yang Wu, S. Nobuhara, K. Nishino
{"title":"Human-Object Interaction Detection with Missing Objects","authors":"Kaen Kogashi, Yang Wu, S. Nobuhara, K. Nishino","doi":"10.23919/MVA51890.2021.9511361","DOIUrl":"https://doi.org/10.23919/MVA51890.2021.9511361","url":null,"abstract":"Existing studies on human-object interaction (HOI) assume that human and object instances can be detected. This paper proposes a more practical HOI detection method for when object instances are not necessarily easily detectable. To our knowledge, we introduce the first method for such challenging HOI detection that incorporates global scene information. The two most widely used public HOI benchmark datasets are shown to contain many cases of HOI with missing objects (HOI-MO). We label these to compose new test sets for the proposed method. The effectiveness and superiority of the proposed method are demonstrated through extensive experiments and comparisons.","PeriodicalId":312481,"journal":{"name":"2021 17th International Conference on Machine Vision and Applications (MVA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127132393","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
Most Influential Paper over the Decade Award 十年最具影响力论文奖
2021 17th International Conference on Machine Vision and Applications (MVA) Pub Date : 2021-07-25 DOI: 10.23919/mva51890.2021.9511394
{"title":"Most Influential Paper over the Decade Award","authors":"","doi":"10.23919/mva51890.2021.9511394","DOIUrl":"https://doi.org/10.23919/mva51890.2021.9511394","url":null,"abstract":"","PeriodicalId":312481,"journal":{"name":"2021 17th International Conference on Machine Vision and Applications (MVA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128527701","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
Video-Based Camera Localization Using Anchor View Detection and Recursive 3D Reconstruction 基于视频的摄像机定位:锚点视图检测和递归三维重建
2021 17th International Conference on Machine Vision and Applications (MVA) Pub Date : 2021-07-07 DOI: 10.23919/MVA51890.2021.9511397
Hajime Taira, Koki Onbe, Naoyuki Miyashita, M. Okutomi
{"title":"Video-Based Camera Localization Using Anchor View Detection and Recursive 3D Reconstruction","authors":"Hajime Taira, Koki Onbe, Naoyuki Miyashita, M. Okutomi","doi":"10.23919/MVA51890.2021.9511397","DOIUrl":"https://doi.org/10.23919/MVA51890.2021.9511397","url":null,"abstract":"In this paper we introduce a new camera localization strategy designed for image sequences captured in challenging industrial situations such as industrial parts inspection. To deal with peculiar appearances that hurt standard 3D reconstruction pipeline, we exploit preknowledge of the scene by selecting key frames in the sequence (called as anchors) which are roughly connected to a certain location. Our method then seek the location of each frame in time-order, while recursively updating an augmented 3D model which can provide current camera location and surrounding 3D structure. In an experiment on a practical industrial situation, our method can localize over 99% frames in the input sequence, whereas standard localization methods fail to reconstruct a complete camera trajectory.","PeriodicalId":312481,"journal":{"name":"2021 17th International Conference on Machine Vision and Applications (MVA)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114377945","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
Predicting Next Local Appearance for Video Anomaly Detection 预测下一个局部外观的视频异常检测
2021 17th International Conference on Machine Vision and Applications (MVA) Pub Date : 2021-06-10 DOI: 10.23919/MVA51890.2021.9511378
Pankaj Raj Roy, Guillaume-Alexandre Bilodeau, Lama Seoud
{"title":"Predicting Next Local Appearance for Video Anomaly Detection","authors":"Pankaj Raj Roy, Guillaume-Alexandre Bilodeau, Lama Seoud","doi":"10.23919/MVA51890.2021.9511378","DOIUrl":"https://doi.org/10.23919/MVA51890.2021.9511378","url":null,"abstract":"We present a local anomaly detection method in videos. As opposed to most existing methods that are computationally expensive and are not very generalizable across different video scenes, we propose an adversarial framework that learns the temporal local appearance variations by predicting the appearance of a normally behaving object in the next frame of a scene by only relying on its current and past appearances. In the presence of an abnormally behaving object, the reconstruction error between the real and the predicted next appearance of that object indicates the likelihood of an anomaly. Our method is competitive with the existing state-of-the-art while being significantly faster for both training and inference and being better at generalizing to unseen video scenes.","PeriodicalId":312481,"journal":{"name":"2021 17th International Conference on Machine Vision and Applications (MVA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126753996","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
Semantic Hierarchy Preserving Deep Hashing for Large-Scale Image Retrieval 基于语义层次的深度哈希大规模图像检索
2021 17th International Conference on Machine Vision and Applications (MVA) Pub Date : 2019-01-31 DOI: 10.23919/MVA51890.2021.9511401
Xuefei Zhe, Ou-Yang Le, Shifeng Chen, Hong Yan
{"title":"Semantic Hierarchy Preserving Deep Hashing for Large-Scale Image Retrieval","authors":"Xuefei Zhe, Ou-Yang Le, Shifeng Chen, Hong Yan","doi":"10.23919/MVA51890.2021.9511401","DOIUrl":"https://doi.org/10.23919/MVA51890.2021.9511401","url":null,"abstract":"Deep hashing models have been proposed as an efficient method for large-scale similarity search. How-ever, most existing deep hashing methods only utilize fine-level labels for training while ignoring the natural semantic hierarchy structure. This paper presents an effective method that preserves the classwise similarity of full-level semantic hierarchy for large-scale image retrieval. Experiments on two benchmark datasets show that our method helps improve the fine-level retrieval performance. Moreover, with the help of the semantic hierarchy, it can produce significantly better binary codes for hierarchical retrieval, which indicates its potential of providing more user-desired retrieval results. The codes are available at https://github.com/mzhang367/hpdh.git.","PeriodicalId":312481,"journal":{"name":"2021 17th International Conference on Machine Vision and Applications (MVA)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126311385","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: 8
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