Rishabh Iyer: Submodular Optimization and Data Summarization with Applications to Computer Vision
“Title: Submodular Optimization and Data Summarization with Applications to Computer Vision”
Visual Data in the form of Images and Videos have been growing at an unprecedented rate in the last few years. While this massive data is a blessing to data science by helping improve predictive accuracy, it is also a curse since humans are unable to consume this large amount of data. Moreover, today, machine generated videos (via Drones, Dash-cams, Body-cams, Security cameras etc.) are being generated at a rate higher than what we as humans can process, and majority of this data is plagued with redundancy. In this talk, I will present a unified framework for Submodular Optimization which provides an end to end solution to these problems. We first show that submodular functions naturally model notions of diversity, coverage, representation and information. Moreover they also lend themselves to practical and provably near optimal algorithms for optimization, thereby providing practical data summarization strategies. Along the way, we will highlight several implementational aspects of submodular optimization, including memoization tricks useful in building real world summarization systems.
We also show how we can efficiently learn submodular functions for different domains and tasks. We will demonstrate the utility of this in summarization tasks related to visual data: Image collection summarization and domain specific video summarization. What comprises a good visual summary depends on the domain at hand — creating a video summary of a soccer game will involve very different modeling characteristics compared to a surveillance video. We try to take a principled approach towards domain specific video summarization, we argue how we can efficiently learn the right weights for the different model families. We shall point out several interesting observations and insights learnt from this characterization. Towards the end of this talk, we shall extend this work to training data subset selection, where we shall show how we can use our summarization framework for reducing training complexity, quick turn-around times for hyper-parameter tuning and Diversified Active Learning.
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