{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bsn-boundary-sensitive-network-for-temporal","title":"BSN: Boundary Sensitive Network for Temporal Action Proposal Generation","arxiv_id":"1806.02964","date":"2018-06-08","proceeding":"ECCV 2018 9","authors":["Tianwei Lin","Xu Zhao","Haisheng Su","Chongjing Wang","Ming Yang"],"abstract":"Temporal action proposal generation is an important yet challenging problem,\nsince temporal proposals with rich action content are indispensable for\nanalysing real-world videos with long duration and high proportion irrelevant\ncontent. This problem requires methods not only generating proposals with\nprecise temporal boundaries, but also retrieving proposals to cover truth\naction instances with high recall and high overlap using relatively fewer\nproposals. To address these difficulties, we introduce an effective proposal\ngeneration method, named Boundary-Sensitive Network (BSN), which adopts \"local\nto global\" fashion. Locally, BSN first locates temporal boundaries with high\nprobabilities, then directly combines these boundaries as proposals. Globally,\nwith Boundary-Sensitive Proposal feature, BSN retrieves proposals by evaluating\nthe confidence of whether a proposal contains an action within its region. We\nconduct experiments on two challenging datasets: ActivityNet-1.3 and THUMOS14,\nwhere BSN outperforms other state-of-the-art temporal action proposal\ngeneration methods with high recall and high temporal precision. Finally,\nfurther experiments demonstrate that by combining existing action classifiers,\nour method significantly improves the state-of-the-art temporal action\ndetection performance.","url_abs":"http://arxiv.org/abs/1806.02964v3","url_pdf":"http://arxiv.org/pdf/1806.02964v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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