Multimodal Multilabel Classification by CLIP

Yanming Guo

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arXiv · 2406.16141

Multimodal Multilabel Classification by CLIP

Yanming Guo

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Abstract

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.

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BibTeX RIS

Yanming Guo. 2024-06-23. Multimodal Multilabel Classification by CLIP. https://arxiv.org/abs/2406.16141

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