Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization
This toolkit addresses the challenge of managing large volumes of visual data for applications such as video sharing platforms and surveillance systems, though it is incremental as it builds on existing submodular function methods.
The authors tackled the problem of visual data selection and summarization by developing Vis-DSS, an open-source toolkit that implements submodular functions for tasks like image collection summarization and video summarization, achieving state-of-the-art performance across multiple benchmarks.
With increasing amounts of visual data being created in the form of videos and images, visual data selection and summarization are becoming ever increasing problems. We present Vis-DSS, an open-source toolkit for Visual Data Selection and Summarization. Vis-DSS implements a framework of models for summarization and data subset selection using submodular functions, which are becoming increasingly popular today for these problems. We present several classes of models, capturing notions of diversity, coverage, representation and importance, along with optimization/inference and learning algorithms. Vis-DSS is the first open source toolkit for several Data selection and summarization tasks including Image Collection Summarization, Video Summarization, Training Data selection for Classification and Diversified Active Learning. We demonstrate state-of-the art performance on all these tasks, and also show how we can scale to large problems. Vis-DSS allows easy integration for applications to be built on it, also can serve as a general skeleton that can be extended to several use cases, including video and image sharing platforms for creating GIFs, image montage creation, or as a component to surveillance systems and we demonstrate this by providing a graphical user-interface (GUI) desktop app built over Qt framework. Vis-DSS is available at https://github.com/rishabhk108/vis-dss