Micro-video Venue Classification Based on Adaptive Weighted Reconstruction
Rui Gong, Jie Guo, Fei Dong, Jianhua Nie, Xiushan Nie · 2023
The development of the Internet has led to the emergence of micro-videos, and they have found favour with a large audience. Micro-video venue classification plays an important role in understanding its content. However, due to privacy protection and other reasons, some data is missing. The semantic strength of each modality is inconsistent in a microvideo, which brings challenges to the accuracy of its venue classification. To address these challenges, this study proposes a Micro-video Venue Classification method based on Adaptive Weighted Reconstruction (MVCAWR). The method complements missing data through reconstruction, and adaptively weights the reconstruction process by calculating the semantic similarity of the acoustic, textual, and visual modalities, respectively. The semantic information of the missing data modality, and the consistency and complementarity among them are preserved to the greatest extent. Experiments show that the proposed method is effective in the task of micro-video venue classification.