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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) DramaQA: °èÃþÀû ÁúÀÇÀÀ´ä°ú ÇÔ²²ÇÏ´Â µîÀåÀι° Á᫐ ºñµð¿À ½ºÅ丮 ÀÌÇØ
¿µ¹®Á¦¸ñ(English Title) DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
ÀúÀÚ(Author) ÀÌÁø¿ì   ¿øÁ¤ÀÓ   À±ÁöÈñ   JinWoo Lee   Jung-Im Won   JeeHee Yoon   ÃÖ¼ºÈ£   ¿Â°æ¿î   ÇãÀ¯Á¤   ÀåÀ¯¿ø   ¼­¾ÆÁ¤   À̽ÂÂù   À̹μö   À庴Ź   Seongho Choi   Kyoung-Woon On   Yu-Jung Heo   Youwon Jang   Ahjeong Seo   Seungchan Lee   Minsu Lee   Byoung-Tak Zhang  
¿ø¹®¼ö·Ïó(Citation) VOL 27 NO. 01 PP. 0001 ~ 0007 (2021. 01)
Çѱ۳»¿ë
(Korean Abstract)
º» ³í¹®Àº ºñµð¿À ½ºÅ丮ÀÇ Æ÷°ýÀû ÀÌÇظ¦ À§ÇÑ »õ·Î¿î ºñµð¿À ÁúÀÇÀÀ´ä µ¥ÀÌÅͼ DramaQA ¸¦ Á¦¾ÈÇÑ´Ù. DramaQA µ¥ÀÌÅͼÂÀº 1) Àΰ£Áö´ÉÀÇ ÀÎÁö ¹ß´Þ ´Ü°è¿¡ ±âÃÊÇÑ ÀΰøÁö´É ½Ã½ºÅÛ¿¡ ´ëÇÑ Æò°¡ ÁöÇ¥·Î¼­ÀÇ °èÃþÀû ÁúÀÇÀÀ´ä µ¥ÀÌÅͼ°ú 2) ½ºÅ丮ÀÇ Áö¿ªÀû ÀÏ°ü¼ºÀ» ¸ðµ¨¸µÇϱâ À§ÇÑ µîÀåÀι° Áß½ÉÀÇ ºñµð¿À ÁÖ¼®À» Á¦°øÇÏ´Â °ÍÀ» ¸ñÇ¥·Î ÇÑ´Ù. DramaQA µ¥ÀÌÅͼÂÀº TV µå¶ó¸¶ ¡°¶Ç ¿ÀÇØ¿µ¡±À» ÀÌ¿ëÇÏ¿© Á¦À۵ǾúÀ¸¸ç, 23,928°³ÀÇ ´Ù¾çÇÑ ±æÀÌÀÇ ºñµð¿À·ÎºÎÅÍ °¢°¢ 4°³ÀÇ ³­À̵µ Áß Çϳª¿¡ Æ÷ÇԵǴ 17,983°³ÀÇ ÁúÀÇÀÀ´ä ½ÖÀ» Æ÷ÇÔÇÑ´Ù. µ¥ÀÌÅͼÂÀº µîÀåÀι° Á᫐ ½Ã°¢Àû ÁÖ¼®ÀÌ µÇ¾îÀÖ´Â 217,308ÀåÀÇ À̹ÌÁöµé°ú »óÈ£ÂüÁ¶°¡ ÇØ°áµÈ ½ºÅ©¸³Æ®¸¦ Á¦°øÇÑ´Ù. ¶ÇÇÑ, ¿ì¸®´Â ºñµð¿À ÁúÀÇÀÀ´ä¿¡ ´ëÇÑ µîÀåÀι°Áß½ÉÀÇ Ç¥ÇöÀ» È¿°úÀûÀ¸·Î ÇнÀÇϱâ À§ÇÑ Dual Matching Multistream ¸ðµ¨À» Á¦¾ÈÇÏ°í DramaQA µ¥ÀÌÅͼ¿¡ Àû¿ëÇÏ¿© µîÀåÀι° Áß½ÉÀÇ ºñµð¿À ½ºÅ丮 ÀÌÇØ ¹æ¹ýÀ» Á¦½ÃÇÑ´Ù.
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(English Abstract)
In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for obtaining a comprehensive understanding of a video story. The DramaQA focuses on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence, and 2) character-centered video annotations to model the local coherence of the story. Our dataset is built upon the TV drama ¡°Another Miss Oh¡± and contains 16,191 QA pairs from 23,928 video clips of various lengths, with each QA pair belonging to one of four difficulty levels. We provide a total of 217,308 annotated images with rich character-centered visual annotations and coreference resolved scripts. In addition, we provide analyses of the dataset as well as a Dual Matching Multistream model which effectively learns character-centered representations of the video to answer questions about the video.
Å°¿öµå(Keyword) Â÷¼¼´ë ½ÃÄö½Ì   º¯ÀÌ ºÐ¼®   Genome Variant Call Format(GVCF) ÆÄÀÏ ¼ÒÆ®/¸ÓÁö   ½ºÆÄÅ©   ºÐ »êº´·Ä󸮠  next-generation sequencing (NGS)   variant analysis   Genome Variant Call Format(GVCF) File Sort/Merge   Spark   parallel/distributed computing   ºñµð¿À ÁúÀÇÀÀ´ä   ºñµð¿À ½ºÅ丮 ÀÌÇØ   ÁúÀÇÀÀ´ä Æò°¡ÁöÇ¥   µîÀåÀι° Á᫐ ºñµð¿À ÁÖ¼®   video question and answering   video story understanding   evaluation metric for QA   character-centered video annotation  
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