2,097 research outputs found
Video Time: Properties, Encoders and Evaluation
Time-aware encoding of frame sequences in a video is a fundamental problem in
video understanding. While many attempted to model time in videos, an explicit
study on quantifying video time is missing. To fill this lacuna, we aim to
evaluate video time explicitly. We describe three properties of video time,
namely a) temporal asymmetry, b)temporal continuity and c) temporal causality.
Based on each we formulate a task able to quantify the associated property.
This allows assessing the effectiveness of modern video encoders, like C3D and
LSTM, in their ability to model time. Our analysis provides insights about
existing encoders while also leading us to propose a new video time encoder,
which is better suited for the video time recognition tasks than C3D and LSTM.
We believe the proposed meta-analysis can provide a reasonable baseline to
assess video time encoders on equal grounds on a set of temporal-aware tasks.Comment: 14 pages, BMVC 201
Creativity: Generating Diverse Questions using Variational Autoencoders
Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as "creativity". In this
paper we propose a creative algorithm for visual question generation which
combines the advantages of variational autoencoders with long short-term memory
networks. We demonstrate that our framework is able to generate a large set of
varying questions given a single input image.Comment: Accepted to CVPR 201
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