Image Embeddings

Image embeddings represent images as numerical vectors, enabling computers to understand and compare visual information. Current research focuses on improving the quality and utility of these embeddings, particularly within the context of vision-language models (like CLIP) and graph neural networks, aiming to enhance tasks such as image retrieval, classification, and generation. This work addresses challenges like handling unseen domains, improving robustness to image distortions, and mitigating biases in embedding spaces, ultimately impacting applications ranging from multimedia search to medical image analysis and financial modeling.