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Binarize CLIP for Multimodal Applications


y embedding is a powerful technique that allows us to convert high-dimensional data into binary vectors. This process allows for efficient storage and faster computation, especially for large-scale multimedia retrieval tasks.

This blog explores the integration of binary embedding within the CLIP framework, aiming to optimize multimodal retrieval and ranking performance. However, the result is not optimal because the pre-trained GCL has not learned to effectively differentiate between positive and negative pairs of samples with 32 times less information. When these embeddings are pseudo-quantized using sigmoid activation, we can apply the negative Hamming distance instead of cosine similarity by measuring the number of differing bits.

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