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[국내논문] 딥러닝 기반 비디오 스토리 학습 기술 원문보기

한국멀티미디어학회지, v.20 no.3, 2016년, pp.23 - 40  

허민오 (서울대 컴퓨터공학부) ,  김경민 (서울대 컴퓨터공학부) ,  장병탁 (서울대학교 공과대학 컴퓨터공학부)

초록이 없습니다.

질의응답

핵심어 질문 논문에서 추출한 답변
영상 컨텐츠 이해 기술이 발전하게 된 세가지 핵심요인은 무엇인가? 이와 같이 영상 컨텐츠 이해 기술이 발전하게 된 세 가지 핵심 요인으로 딥러닝 기술 외에도 GPU 등의 하드웨어로 인한 계산 속도의 향상, 학습에 사용할 수 있는 빅데이터의 등장을 들 수 있다. 즉, 빅데이터를 재료로 딥러닝 기술을 이용하여 정보의 다양한 표현 방식을 학습하고 영상의 다양한 요소를 인식하는 지능 기술의 비약적인 발전이 최근 계속되고 있다.
CNN을 통한 모델링이 이미지 데이터에 적합한 이유는? 이미지 데이터의 경우 이러한 특징에 매우 적합하다. 왜냐하면 이미지는 국부적 패턴이 모여 전체적 패턴을 구성하는 계층적 특성이 있으며, 이 국부적 패턴은 위치에 무관하게 전 영역에서 동일하게 사용될 수 있다. 만약, 이미지 안에 선으로 그린 사과가 있다고 할 때, 사과를 표현하는 윤곽선은 사과의 좌우측 테두리에 상관없이 동일한 패턴을 쓸 수 있다.
R-CNN 기법의 특징은 무엇인가? 물체검출 문제에도 컨볼루션 연산을 이용한 딥러닝기법인 R-CNN[3]이 제안되었다. 단순히 이미지 안에 어떤 물체가 있는지만 다루지 않고 무엇이 어디에 있는지 검출해낸다. 이미지를 격자형 영역으로 나누고 각 영역이 검출영역에 속하는지 여부를 다루도록 한 영역제안망(region proposal network)을 모델 안에 포함한 것이 특징이다.
질의응답 정보가 도움이 되었나요?

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