Leveraging Embeddings and Clustering Techniques in Computer Vision

Embeddings are gaining prominence in natural language processing and computer vision, offering advanced methods for analyzing and managing datasets. The blog post discusses the application of embeddings in computer vision, focusing on clustering MNIST images using pixel brightness and dimensionality reduction techniques like t-SNE and UMAP, which help visualize high-dimensional data by preserving the relative similarity between data points. The comparison of t-SNE and UMAP reveals that UMAP is more computationally efficient and better at preserving global structures, whereas t-SNE focuses on local relationships. For more complex images, pixel brightness alone is insufficient, and OpenAI's CLIP embeddings provide a more abstract and compact representation, capturing high-level visual and semantic information. These embeddings facilitate tasks such as identifying similar images through cosine similarity measures. The post highlights the potential of CLIP embeddings in computer vision and hints at future explorations into new models and use cases to further leverage embeddings' capabilities.