Visual Question Answering with Question Representation Update (QRU)
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摘要Our method aims at reasoning over natural language questions and visual images. Given a natural language question about an image, our model updates the question representation iteratively by selecting image regions relevant to the query and learns to give the correct answer. Our model contains several reasoning layers, exploiting complex visual relations in the visual question answering (VQA) task. The proposed network is end-to-end trainable through back-propagation, where its weights are initialized using pre-trained convolutional neural network (CNN) and gated recurrent unit (GRU). Our method is evaluated on challenging datasets of COCO-QA [19] and VQA [2] and yields state-of-the-art performance.
著者Ruiyu LI, Jiaya JIA
會議名稱The 30th Conference on Neural Information Processing Systems (NIPS 2016)
會議開始日04.12.2016
會議完結日09.12.2016
會議地點Barcelona
會議國家/地區西班牙
出版年份2016
語言美式英語

上次更新時間 2018-18-01 於 02:18