We study the problem of selecting a subset of big data to train a classifier while incurring
minimal performance loss. We show the connection of submodularity to the data likelihood …

Learning image similarity metrics in an end-to-end fashion with deep networks has
demonstrated excellent results on tasks such as clustering and retrieval. However, current …

We present a novel method for summarizing raw, casually captured videos. The objective is
to create a short summary that still conveys the story. It should thus be both, interesting and …

In this manuscript, we offer a gentle review of submodularity and supermodularity and their
properties. We offer a plethora of submodular definitions; a full description of a number of …

Although video summarization has achieved tremendous success benefiting from Recurrent
Neural Networks (RNN), RNN-based methods neglect the global dependencies and multi …

Can we summarize multi-category data based on user preferences in a scalable manner?
Many utility functions used for data summarization satisfy submodularity, a natural …

Abstract Information-theoretic quantities like entropy and mutual information have found
numerous uses in machine learning. It is well known that there is a strong connection …

This paper presents a video summarization technique for an Internet video to provide a
quick way to overview its content. This is a challenging problem because finding important …

Given the availability of abundant data, deep learning models have been advanced and
become ubiquitous in the past decade. In practice, due to many different reasons (eg …

In this paper, we build a general summarization framework for both of edited video and raw
video summarization. Overall, our work can be divided into three folds. 1) Four models are …