Zero-shot Micro-video Classification with Dual Alignment Topic Model

Abstract

In the current digital media environment, micro-video platforms such as TikTok and Kuaishou have become major channels for content consumption and social interaction. Micro-video classification is crucial for the performance of personalized recommendation systems. However, traditional micro-video classification methods rely on a large amount of labeled data, making it challenging to handle the dynamic emergence of new categories and topics. Therefore, zero-shot micro-video classification has become an important research topic. This paper proposes a Dual Alignment Topic Model (DATM) model that achieves knowledge transfer from known categories to unknown categories through dual topic mining and alignment constraints. Specifically, we use a pre-trained language model to generate word embeddings for class semantic descriptions (CSD) and perform topic modeling on both videos and CSDs. By employing distribution alignment and reconstruction alignment constraints, we effectively mitigate the transfer problem between videos and CSDs. Extensive experiments on real-world industrial datasets validate the effectiveness of our model, significantly improving the performance of zero-shot micro-video classification.