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Effects of multi-modality on sentence generation by random hypergraph memory model
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랜덤 하이퍼그래프 메모리 모델을 이용한 문장생성에 멀티 모달리티가 미치는 영향

논문 기본 정보

Type
Proceeding
Author
Woongchang Yoon (서울대학교) Byoung-Tak Zhang (서울대학교)
Journal
The Institute of Electronics and Information Engineers 대한전자공학회 학술대회 대한전자공학회 2010년 하계종합학술대회
Published
2010.6
Pages
1,483 - 1,486 (4page)

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Effects of multi-modality on sentence generation by random hypergraph memory model
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Research history (4)

2010

Image annotation method in video corpus using Entropy based Multi-modal Random Hypergraph Learning
Woongchang Yoon ,  Ho-Sik Seok ,  Byoung-Tak Zhang 한국정보과학회 학술발표논문집 2010.11 Proceeding
Effects of multi-modality on sentence generation by random hypergraph memory model
Woongchang Yoon ,  Byoung-Tak Zhang 대한전자공학회 학술대회 2010.06 Proceeding
Effects of multi-modality on sentence generation by random hypergraph memory model
Woongchang Yoon ,  Byoung-Tak Zhang 대한전자공학회 학술대회 2010.06 Proceeding
Sentence generation on sequential multi-modal data using random hypergraph model
Woongchang Yoon ,  Byoung-Tak Zhang 한국정보과학회 학술발표논문집 2010.06 Proceeding
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Abstract· Keywords

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In this paper, we introduce a generation method based on the random hypergraph memory model using multimodal cue. In order to demonstrate the effects of multi-modal cue on sentence generation, we compare experimental results of the sentence generation given only text or image cues and image-text multi-modal pair cues. For experiments, we use TV drama screen shots and script data. The image-text corpus are converted into a set of weighted hyperedge. Our aim is to generate sentences using cues based on these set of hyperedges. It is our experiment's result show that language generation is helped by multi-modality data more than only text or image data. We accomplish to simulate effects of supporting memory improvement by multi-modal data set without templates or rote learning.

Contents

Abstract
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 실험 결과
Ⅳ. 결론 및 향후 연구 방향
감사의 글
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UCI(KEPA) : I410-ECN-0101-2010-569-003172822