From text question-answering to multimedia QA on web-scale media resources
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Exploring large scale data for multimedia QA: an initial study
Proceedings of the ACM International Conference on Image and Video Retrieval
Multimedia answering: enriching text QA with media information
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Snap-and-ask: answering multimodal question by naming visual instance
Proceedings of the 20th ACM international conference on Multimedia
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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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.