Compressible and Searchable: AI-native Multi-Modal Retrieval System with
  Learned Image Compression
Jixiang Luo
The burgeoning volume of digital content across diverse modalities
necessitates efficient storage and retrieval methods. Conventional approaches
struggle to cope with the escalating complexity and scale of multimedia data.
In this paper, we proposed framework addresses this challenge by fusing
AI-native multi-modal search capabilities with neural image compression. First
we analyze the intricate relationship between compressibility and
searchability, recognizing the pivotal role each plays in the efficiency of
storage and retrieval systems. Through the usage of simple adapter is to bridge
the feature of Learned Image Compression(LIC) and Contrastive Language-Image
Pretraining(CLIP) while retaining semantic fidelity and retrieval of
multi-modal data. Experimental evaluations on Kodak datasets demonstrate the
efficacy of our approach, showcasing significant enhancements in compression
efficiency and search accuracy compared to existing methodologies. Our work
marks a significant advancement towards scalable and efficient multi-modal
search systems in the era of big data.