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hello everyone the title of our paper is SSB semisupervised video object detection with SP annotations I am tan Mahmud my coauthor S chunu and Di I am from the University of Texas at Austin letamp;#39;s introduce the problem supervised video object detection requires dance annotations on several time steps of videos so in figurea we can see the supervis method requires annotations in several time steps but with the first annotations the supervis method generates suboptimal performance for insufficient temporal exploration so when we have only two step labeled frames for the over the video we cannot explore different time steps in the supervis method but the ssvd leverages semi-supervised techniques to estimate robot sudo labels across different time steps by using the spse annotations so by using only two SP annotations we can estimate some the some sud labels in other time steps of the video so can we so that we can explore the all time steps or different Tim steps over the video so
