Sitcom-Stars Oriented Video Advertising via Clothing Retrieval
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This paper introduces a novel learning-based framework for video content-based advertising, DeepLink, which aims at linking sitcom-stars and online stores with clothing retrieval by using state-of-the-art deep convolutional neural networks (CNNs). Concretely, several deep CNN models are adopted for composing multiple sub-modules in DeepLink, including human-body detection, human-pose selection, face verification, clothing detection and retrieval from advertisements (ads) pool that is constructed by clothing images collected from real-world online stores. For clothing detection and retrieval from ad images, we firstly transfer the state-of-the-art deep CNN models to our data domain, and then train corresponding models based on our constructed large-scale clothing datasets. Extensive experimental results demonstrate the feasibility and efficacy of our proposed clothing-based video-advertising system.
KeywordsVideo advertising Deep learning Object detection Face verification Image retrieval Clothing detection
This work was supported in part by the Natural Science Foundation of China under Grant 61572156 and in part by the Shenzhen Science and Technology Program under Grant JCYJ20170413105929681.
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