Hierarchical Patch Compression for ColPali: Efficient Multi-Vector Document Retrieval with Dynamic Pruning and Quantization

TL;DR

HPC-ColPali introduces a hierarchical patch compression framework that significantly reduces storage and computation costs in multi-vector document retrieval systems while maintaining high accuracy, enabling faster and more scalable retrieval.

Contribution

The paper presents a novel hierarchical patch compression method combining quantization, dynamic pruning, and binary encoding to improve efficiency in multi-vector retrieval systems.

Findings

Achieves 30-50% lower query latency with maintained retrieval precision.

Reduces storage by up to 32x through K-Means quantization.

Decreases late-interaction computation by up to 60% with minimal accuracy loss.

Abstract

Multi-vector document retrieval systems, such as ColPali, excel in fine-grained matching for complex queries but incur significant storage and computational costs due to their reliance on high-dimensional patch embeddings and late-interaction scoring. To address these challenges, we propose HPC-ColPali, a Hierarchical Patch Compression framework that enhances the efficiency of ColPali while preserving its retrieval accuracy. Our approach integrates three innovative techniques: (1) K-Means quantization, which compresses patch embeddings into 1-byte centroid indices, achieving up to 32$\times$ storage reduction; (2) attention-guided dynamic pruning, utilizing Vision-Language Model attention weights to retain only the top-$p\%$ most salient patches, reducing late-interaction computation by up to 60\% with less than 2\% nDCG@10 loss; and (3) optional binary encoding of centroid indices into $b$-bit strings ($b=\lceil\log_2 K\rceil$), enabling rapid Hamming distance-based similarity search for resource-constrained environments. Evaluated on the ViDoRe and SEC-Filings datasets, HPC-ColPali achieves 30--50\% lower query latency under HNSW indexing while maintaining high retrieval precision. When integrated into a Retrieval-Augmented Generation pipeline for legal summarization, it reduces hallucination rates by 30\% and halves end-to-end latency. These advancements establish HPC-ColPali as a scalable and efficient solution for multi-vector document retrieval across diverse applications. Code is available at https://github.com/DngBack/HPC-ColPali.