Compressible And Searchable: Ai-native Multi-modal Retrieval System With Learned Image Compression

Abstract

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 advancem

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyluo2024compressible

Related papers

• A Multimodal Deep Learning Framework For Scalable Content Based Visual Media Retrieval (2021)0.00
• Leaner And Faster: Two-stage Model Compression For Lightweight Text-image Retrieval (2022)6.34
• Enhancing Image Retrieval : A Comprehensive Study On Photo Search Using The CLIP Mode (2024)0.00
• Learning To Compress And Search Visual Data In Large-scale Systems (2019)0.00
• Joint Fusion And Encoding: Advancing Multimodal Retrieval From The Ground Up (2025)0.00
• Composed Multi-modal Retrieval: A Survey Of Approaches And Applications (2025)3.88
• CLIP Multi-modal Hashing For Multimedia Retrieval (2024)3.58
• Multimodal Learned Sparse Retrieval For Image Suggestion (2024)0.00