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The multi-modal hashing method is widely used in multimedia retrieval. It canfuse multi-source data to generate binary hash code. However, the currentmulti-modal methods have the problem of low retrieval accuracy. The reason isthat the individual backbone networks have limited feature expressioncapabilities and are not jointly pre-trained on large-scale unsupervisedmulti-modal data. To solve this problem, we propose a new baseline CLIPMulti-modal Hashing (CLIPMH) method. It uses CLIP model to extract text andimage features, and then fuse to generate hash code. CLIP improves theexpressiveness of each modal feature. In this way, it can greatly improve theretrieval performance of multi-modal hashing methods. In comparison tostate-of-the-art unsupervised and supervised multi-modal hashing methods,experiments reveal that the proposed CLIPMH can significantly enhanceperformance (Maximum increase of 8.38%). CLIP also has great advantages overthe text and visual backbone networks commonly used before.

CLIP Multi-modal Hashing: A new baseline CLIPMH