Binary embedding is a powerful technique to convert high-dimensional data into binary vectors, enabling efficient storage and computation for large-scale multimedia tasks. Compression from float32 to binary can reduce memory usage by 32 times. This blog explores integrating binary embedding in the CLIP framework to enhance multimodal retrieval and ranking performance. Test-time binary quantization impacts CLIP performance significantly, with packing bits yielding ineffective results. Incorporating binary quantization in training, particularly with sigmoid activation, improves outcomes. Float embeddings’ performance is well-preserved despite changes, and Hamming distance slightly outperforms cosine similarity for binary embeddings. Repeatable experimenting with quantization scale is vital for optimal performance.
https://www.marqo.ai/blog/learn-to-binarize-clip-for-multimodal-retrieval-and-ranking