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The code for the paper "Contrastive Quantization with Code Memory for Unsupervised Image Retrieval" (AAAI'22, Oral).

Python 88.40% Shell 11.60%
contrastive-learning deep-hashing hashing image-retrieval quantization unsupervised-learning aaai aaai2022

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aaai22-mecoq's Issues

Can you provide the Appendix you mentioned in the paper?

In your paper, you said "We proof Proposition 1 in Appendix”, but I can't find the appendix on neither arXiv nor AAAI. So, can you provide the Appendix you mentioned in your paper? Or provide detailed proof of Proposition 1.

A Question about diversity of codebook

Dear authors, I'm looking into the proposed paper, and I have a question about the loss term which encourages codebook diversity. I notice that the green curve in figure 2b remains to be above zero all the time. And I assume this loss term pushes codes in a codebook to be as orthogonal to each other as possible and therefore should be minimized to 0 (pls correct me if I'm wrong).

My question is: do you have any operation which constrain the codes in a codebook to be vectors containing only non-negative values?

Appreciate your answer in advance:)

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