GCNBoost: Artwork Classificationby Label Propagation Through a Knowledge Graph

概要

Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formulate VideoQA as the joint understanding of visual and language information. However, this training procedure is costly and still less competent with human performance. In this paper, we investigate a transfer learning method by the introduction of domain-agnostic knowledge and domain-specific knowledge. First, we develop a novel transfer learning framework, which finetunes the pre-trained model by applying domain-agnostic knowledge as the medium. Second, we construct a new VideoQA dataset with 21,412 human-generated question-answer samples for comparable transfer of knowledge. Our experiments show that: (i) domain-agnostic knowledge is transferable and (ii) our proposed transfer learning framework can boost VideoQA performance effectively.

論文種別
発表文献
Proc.~ACM International Conference on Multimedia Retrieval (ICMR)
Noa Garcia
Noa Garcia
特任助教

Her research interests lie in computer vision and machine learning applied to visual retrieval and joint models of vision and language for high-level understanding tasks.

Benjamin Renoust
Benjamin Renoust
招へい准教授
Chenhui Chu
Chenhui Chu
招へい准教授
中島悠太
中島悠太
准教授

コンピュータビジョン・パターン認識などの研究。ディープニューラルネットワークなどを用いた画像・映像の認識・理解を主に、自然言語処理を援用した応用研究などに従事。

長原一
長原一
教授

コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。