Multimodal Multilabel Classification

Multimodal multilabel classification tackles the challenge of assigning multiple labels to data encompassing diverse modalities like images, text, and tabular data. Current research emphasizes effective fusion strategies for combining information from these different sources, often employing deep learning architectures such as convolutional neural networks (for images), transformer networks (for text), and gradient boosting (for tabular data), sometimes pre-trained on large datasets and fine-tuned for specific tasks. This field is significant for its applications across various domains, including cultural heritage preservation, healthcare (e.g., food classification), and e-commerce, where accurate and efficient classification of complex data is crucial. The development of robust benchmarks and publicly available datasets is also driving progress in this area.