# Compressible and Searchable: AI-native Multi-Modal Retrieval System with Learned Image Compression

arXiv: https://arxiv.org/abs/2404.10234 · HTML: https://mlsanity.com/paper/2404.10234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [Instance-level Image Retrieval using Reranking Transformers](https://mlsanity.com/paper/2103.12236/) (2021)
- [PO-ELIC: Perception-Oriented Efficient Learned Image Coding](https://mlsanity.com/paper/2205.14501/) (2022)
- [Exploiting Local Features from Deep Networks for Image Retrieval](https://mlsanity.com/paper/1504.05133/) (2015)
- [Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization](https://mlsanity.com/paper/2403.13030/) (2024)
- [Evaluating the Practicality of Learned Image Compression](https://mlsanity.com/paper/2207.14524/) (2022)

## Built on by (most cited first, 1 of 1)

- [Rethinking Learned Image Compression: Context is All You Need](https://mlsanity.com/paper/2407.11590/) (2024)

## Abstract (arXiv)

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.