KEYWORDS: Data modeling, Video, Scene classification, Neural networks, Performance modeling, Visualization, Image classification, Process modeling, Target recognition, Visual process modeling
Video is often accompanied by advertisement recommendation, which is an important part of it. In order to make the recommendation of advertisements intelligent, it is important to know the categories of scenes in videos. Although scene recognition and classification have been extensively studied, most methods require a large amount of data sets and training time. To address this issue, we adopt transfer learning t, which has achieved great success in visual tasks with high accuracy and small data set. In this scheme, we propose a model which can be applied to intelligent recommendation of advertisements. We chose class places from taskonomy as our source task model, and it has relatively good accuracy after freezing and training. Our model is not only suitable for indoor scenes, but also suitable for several outdoor scenes which often appear in video and have advertising value.
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