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Text Synopsis Generation for Egocentric Videos by Sharghi, Aidean; Lobo, Niels da Vitoria; Shah, Mubarak is a scholarly article available to read on EtoBox.

What is Text Synopsis Generation for Egocentric Videos about?

Mass utilization of body-worn cameras has led to a huge corpus of available egocentric video. Existing video summarization algorithms can accelerate browsing such videos by selecting (visually) interesting shots from them. Nonetheless, since the system user still has to watch the summary videos, browsing large video databases remain a challenge. Hence, in this work, we propose to generate a textual synopsis, consisting of a few sentences describing the most important events in a long egocentric videos. Users can read the short text to gain insight about the video, and more importantly, efficiently search through the content of a large video database using text queries. Since egocentric videos are long and contain many activities and events, using video-to-text algorithms results in thousands of descriptions, many of which are incorrect. Therefore, we propose a multi-task learning scheme to simultaneously generate descriptions for video segments and summarize the resulting descriptions in an end-to-end fashion. We Input a set of video shots and the network generates a text description for each shot. Next, visual-language content matching unit that is trained with a weakly supervised

Author

Sharghi, Aidean; Lobo, Niels da Vitoria; Shah, Mubarak

Published

2020

Language

EN