Summarizing First-Person Videos from Third Persons' Points of View

HSUAN-I HO, Wei-Chen Chiu, Yu-Chiang Frank Wang; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 70-85

Abstract


Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage. However, most existing studies rely on training data of third-person videos, which cannot easily generalize to highlight the first-person ones. With the goal of deriving an effective model to summarize first-person videos, we propose a novel deep neural network architecture for describing and discriminating vital spatiotemporal information across videos with different points of view. Our proposed model is realized in a semi-supervised setting, in which fully annotated third-person videos, unlabeled first-person videos, and a small amount of annotated first-person ones are presented during training. In our experiments, qualitative and quantitative evaluations on both benchmarks and our collected first-person video datasets are presented.

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[bibtex]
@InProceedings{HO_2018_ECCV,
author = {HO, HSUAN-I and Chiu, Wei-Chen and Frank Wang, Yu-Chiang},
title = {Summarizing First-Person Videos from Third Persons' Points of View},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}