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A Robust Passage Retrieval Algorithm for Video Question Answering

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2 Author(s)
Yu-Chieh Wu ; Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Jhongli ; Jie-Chi Yang

In this paper, we present a robust passage retrieval algorithm to extend the conventional text question answering (Q/A) to videos. Users interact with our videoQ/A system through natural language queries, while the top-ranked passage fragments with associated video clips are returned as answers. We compare our method with five of the high-performance ranking algorithms that are portable to different languages and domains. The experiments were evaluated with 75.3 h of Chinese videos and 253 questions. The experimental results showed that our method outperformed the second best retrieval model (language models) in relatively 1.43% in mean reciprocal rank (MRR) score and 11.36% when employing a Chinese word segmentation tool. By adopting the initial retrieval results from the retrieval models, our method yields an improvement of at least 5.94% improvement in MRR score. This makes it very attractive for the Asia-like languages since the use of a well-developed word tokenizer is unnecessary.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:18 ,  Issue: 10 )

Date of Publication:

Oct. 2008

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