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With increasing amounts of visual data being created in the form of videosand images, visual data selection and summarization are becoming everincreasing problems. We present Vis-DSS, an open-source toolkit for Visual DataSelection and Summarization. Vis-DSS implements a framework of models forsummarization and data subset selection using submodular functions, which arebecoming increasingly popular today for these problems. We present severalclasses of models, capturing notions of diversity, coverage, representation andimportance, along with optimization/inference and learning algorithms. Vis-DSSis the first open source toolkit for several Data selection and summarizationtasks including Image Collection Summarization, Video Summarization, TrainingData selection for Classification and Diversified Active Learning. Wedemonstrate state-of-the art performance on all these tasks, and also show howwe can scale to large problems. Vis-DSS allows easy integration forapplications to be built on it, also can serve as a general skeleton that canbe extended to several use cases, including video and image sharing platformsfor creating GIFs, image montage creation, or as a component to surveillancesystems and we demonstrate this by providing a graphical user-interface (GUI)desktop app built over Qt framework. Vis-DSS is available athttps://github.com/rishabhk108/vis-dss

Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization