  Multimodal multilabel classification (MMC) is a challenging task that aims to
design a learning algorithm to handle two data sources, the image and text, and
learn a comprehensive semantic feature presentation across the modalities. In
this task, we review the extensive number of state-of-the-art approaches in MMC
and leverage a novel technique that utilises the Contrastive Language-Image
Pre-training (CLIP) as the feature extractor and fine-tune the model by
exploring different classification heads, fusion methods and loss functions.
Finally, our best result achieved more than 90% F_1 score in the public Kaggle
competition leaderboard. This paper provides detailed descriptions of novel
training methods and quantitative analysis through the experimental results.
