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Automatic video description requires the generation of natural languagestatements about the actions, events, and objects in the video. An importanthuman trait, when we describe a video, is that we are able to do this withvariable levels of detail. Different from this, existing approaches forautomatic video descriptions are mostly focused on single sentence generationat a fixed level of detail. Instead, here we address video description ofmanipulation actions where different levels of detail are required for beingable to convey information about the hierarchical structure of these actionsrelevant also for modern approaches of robot learning. We propose one hybridstatistical and one end-to-end framework to address this problem. The hybridmethod needs much less data for training, because it models statisticallyuncertainties within the video clips, while in the end-to-end method, which ismore data-heavy, we are directly connecting the visual encoder to the languagedecoder without any intermediate (statistical) processing step. Both frameworksuse LSTM stacks to allow for different levels of description granularity andvideos can be described by simple single-sentences or complex multiple-sentencedescriptions. In addition, quantitative results demonstrate that these methodsproduce more realistic descriptions than other competing approaches.

Multi Sentence Description of Complex Manipulation Action Videos