A duration-based policy table selects between thresholding and fixed-interval splitting for scene detection, and a sharpness-plus-brightness score picks one keyframe per scene.
Uncovering Temporal Context for Video Question and Answering
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abstract
In this work, we introduce Video Question Answering in temporal domain to infer the past, describe the present and predict the future. We present an encoder-decoder approach using Recurrent Neural Networks to learn temporal structures of videos and introduce a dual-channel ranking loss to answer multiple-choice questions. We explore approaches for finer understanding of video content using question form of "fill-in-the-blank", and managed to collect 109,895 video clips with duration over 1,000 hours from TACoS, MPII-MD, MEDTest 14 datasets, while the corresponding 390,744 questions are generated from annotations. Extensive experiments demonstrate that our approach significantly outperforms the compared baselines.
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Scene Detection Policies and Keyframe Extraction Strategies for Large-Scale Video Analysis
A duration-based policy table selects between thresholding and fixed-interval splitting for scene detection, and a sharpness-plus-brightness score picks one keyframe per scene.