{"id":3081,"date":"2024-01-22T09:43:28","date_gmt":"2024-01-22T01:43:28","guid":{"rendered":"https:\/\/www.1ai.net\/?p=3081"},"modified":"2024-01-22T09:43:28","modified_gmt":"2024-01-22T01:43:28","slug":"%e5%ad%97%e8%8a%82%e8%b7%b3%e5%8a%a8%e6%8f%90%e5%87%ba%e6%96%b0%e6%96%b9%e6%b3%95gpe-ai%e7%9c%8b%e8%a7%86%e9%a2%91%e5%8f%af%e8%87%aa%e5%8a%a8%e6%89%be%e9%ab%98%e8%83%bd%e6%97%b6%e5%88%bb","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/3081.html","title":{"rendered":"ByteDance proposes a new method GPE AI can automatically find "high-energy moments" when watching videos"},"content":{"rendered":"

AI Technology<\/a>Its application in the video field has always attracted much attention. Through AI's rapid detection of highlight clips in videos, viewers can directly jump to the exciting moments, and anchors can also review their own performances.ByteDance<\/a>In collaboration with the Institute of Automation of the Chinese Academy of Sciences, we annotated the LiveFood food video dataset for domain incremental learning and proposed a solution based on prototype learning. This method uses the solution of highlight prototype learning to perform a binary classification task at the video frame level to determine whether the video frame is a highlight or non-highlight, and achieves good highlight detection performance. Through these efforts, the prospects for the application of AI technology in the video field are broader.<\/p>\n

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By using AI to quickly detect highlights in videos, viewers can go directly to the exciting moments, and anchors can review their performances. To address the difficulties of incremental learning in the video domain, ByteDance and the Institute of Automation of the Chinese Academy of Sciences annotated the food video dataset LiveFood and proposed a solution based on prototype learning.<\/p>\n

ByteDance and the Institute of Automation of the Chinese Academy of Sciences proposed a new method to use AI to quickly detect highlight clips in videos, and to flexibly extract the length of input videos and highlight lengths. At the same time, they annotated the food video dataset LiveFood for domain incremental learning and proposed a solution based on prototype learning. The application prospects of AI technology in the video field are even broader.<\/p>\n

ByteDance and the Institute of Automation of the Chinese Academy of Sciences proposed a new method to use AI to quickly detect highlight clips in videos, and to achieve flexible extraction of input video length and highlight length. This method has achieved good highlight detection performance and is of great significance to the incremental learning problem in the video field, opening up a new situation for the application of AI technology in the video field.<\/p>","protected":false},"excerpt":{"rendered":"

The application of AI technology in the video field has been attracting much attention, and the rapid detection of highlights in the video through AI can realize that the audience can be directly parachuted into the exciting moments, and the anchor can review his performance. In response to the dilemma of incremental learning in the video domain, ByteDance, in conjunction with the Institute of Automation of the Chinese Academy of Sciences, labeled LiveFood, a food video dataset for incremental learning in the domain, and proposed a solution based on prototype learning. The method uses a highlight prototype learning scheme to do a binary classification task at the video frame level to determine whether a video frame belongs to highlight or non-highlight, and achieves good highlight detection performance. With these efforts, the prospect of AI technology application in the video field is more promising. Through AI's fast detection of highlights in video, viewers can directly airborne to the exciting moments, and anchors can also review the<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[161,548],"collection":[],"class_list":["post-3081","post","type-post","status-publish","format-standard","hentry","category-news","tag-ai","tag-548"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/3081","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=3081"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/3081\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=3081"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=3081"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=3081"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=3081"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}