12,819 research outputs found
DeepStory: Video Story QA by Deep Embedded Memory Networks
Question-answering (QA) on video contents is a significant challenge for
achieving human-level intelligence as it involves both vision and language in
real-world settings. Here we demonstrate the possibility of an AI agent
performing video story QA by learning from a large amount of cartoon videos. We
develop a video-story learning model, i.e. Deep Embedded Memory Networks
(DEMN), to reconstruct stories from a joint scene-dialogue video stream using a
latent embedding space of observed data. The video stories are stored in a
long-term memory component. For a given question, an LSTM-based attention model
uses the long-term memory to recall the best question-story-answer triplet by
focusing on specific words containing key information. We trained the DEMN on a
novel QA dataset of children's cartoon video series, Pororo. The dataset
contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained
sentences for scene description, and 8,913 story-related QA pairs. Our
experimental results show that the DEMN outperforms other QA models. This is
mainly due to 1) the reconstruction of video stories in a scene-dialogue
combined form that utilize the latent embedding and 2) attention. DEMN also
achieved state-of-the-art results on the MovieQA benchmark.Comment: 7 pages, accepted for IJCAI 201
Quantum Key Distribution with Blind Polarization Bases
We propose a new quantum key distribution scheme that uses the blind
polarization basis. In our scheme the sender and the receiver share key
information by exchanging qubits with arbitrary polarization angles without
basis reconciliation. As only random polarizations are transmitted, our
protocol is secure even when a key is embedded in a not-so-weak coherent-state
pulse. We show its security against the photon number splitting attack and the
impersonation attack.Comment: Security has been improved upon referee's comment. 4 pages and 2
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