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The thumbnail, as the first sight of a micro-video, plays a pivotal role inattracting users to click and watch. While in the real scenario, the more thethumbnails satisfy the users, the more likely the micro-videos will be clicked.In this paper, we aim to select the thumbnail of a given micro-video that meetsmost users` interests. Towards this end, we present a multi-labelvisual-semantic embedding model to estimate the similarity between the pair ofeach frame and the popular topics that users are interested in. In this model,the visual and textual information is embedded into a shared semantic space,whereby the similarity can be measured directly, even the unseen words.Moreover, to compare the frame to all words from the popular topics, we devisean attention embedding space associated with the semantic-attention projection.With the help of these two embedding spaces, the popularity score of a frame,which is defined by the sum of similarity scores over the corresponding visualinformation and popular topic pairs, is achieved. Ultimately, we fuse thevisual representation score and the popularity score of each frame to selectthe attractive thumbnail for the given micro-video. Extensive experimentsconducted on a real-world dataset have well-verified that our modelsignificantly outperforms several state-of-the-art baselines.

Towards Micro-video Thumbnail Selection via a Multi-label Visual-semantic Embedding Model