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

当前的多模态方法存在检索精度低的问题，本文提出了一种新的基准 CLIP 多模态哈希方法，通过使用 CLIP 模型提取文本和图像特征，并将它们融合生成哈希编码，该方法显著提高了多模态哈希方法的检索性能。