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arXiv · 1403.6173

Coherent Multi-Sentence Video Description with Variable Level of Detail

Anna Senina·Marcus Rohrbach·Wei Qiu·Annemarie Friedrich·Sikandar Amin·Mykhaylo Andriluka·Manfred Pinkal·Bernt Schiele
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Abstract

Humans can easily describe what they see in a coherent way and at varying level of detail. However, existing approaches for automatic video description are mainly focused on single sentence generation and produce descriptions at a fixed level of detail. In this paper, we address both of these limitations: for a variable level of detail we produce coherent multi-sentence descriptions of complex videos. We follow a two-step approach where we first learn to predict a semantic representation (SR) from video and then generate natural language descriptions from the SR. To produce consistent multi-sentence descriptions, we model across-sentence consistency at the level of the SR by enforcing a consistent topic. We also contribute both to the visual recognition of objects proposing a hand-centric approach as well as to the robust generation of sentences using a word lattice. Human judges rate our multi-sentence descriptions as more readable, correct, and relevant than related work. To understand the difference between more detailed and shorter descriptions, we collect and analyze a video description corpus of three levels of detail.

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Anna Senina, Marcus Rohrbach, Wei Qiu, Annemarie Friedrich, Sikandar Amin, Mykhaylo Andriluka, Manfred Pinkal, Bernt Schiele. 2014-03-24. Coherent Multi-Sentence Video Description with Variable Level of Detail. https://doi.org/10.1007/978-3-319-11752-2_15

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