Multimodal Embeddings

Multimodal embeddings integrate information from different data types, such as text, images, and audio, into a unified representation, aiming to improve the understanding and analysis of complex data. Current research focuses on developing and refining model architectures, including transformer-based models and graph neural networks, to effectively fuse these modalities and mitigate biases inherent in individual embedding spaces. This work is significant for advancing various applications, from improving the accuracy of image-text retrieval and visual grounding to enhancing medical diagnosis and earth observation through more comprehensive data analysis. The ability to create robust and unbiased multimodal embeddings is crucial for building more reliable and fair AI systems.