Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework
arXiv:2602.19549v1 Announce Type: new Abstract: Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current multimodal retrieval applications. The state-of-the-art multi-vector paradigm excels in performance but suffers from prohibitive overhead, a problem that current efficiency methods like pruning and merging address imperfectly, creating a difficult trade-off between compression rate and feature fidelity. To overcome this dilemma, we introduce Prune-then-Merge, a novel two-stage framework that synergizes these complementary approaches. Our method first employs an adaptive pruning stage to filter out low-information patches, creating a refined, high-signal set of embeddings. Subsequently, a hierarchical merging stage compresses this pre-filtered set, effectively summarizing semantic content without the noise-induced feature dilution seen in single-stage methods. Extensive experiments on 29 VDR
arXiv:2602.19549v1 Announce Type: new Abstract: Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current multimodal retrieval applications. The state-of-the-art multi-vector paradigm excels in performance but suffers from prohibitive overhead, a problem that current efficiency methods like pruning and merging address imperfectly, creating a difficult trade-off between compression rate and feature fidelity. To overcome this dilemma, we introduce Prune-then-Merge, a novel two-stage framework that synergizes these complementary approaches. Our method first employs an adaptive pruning stage to filter out low-information patches, creating a refined, high-signal set of embeddings. Subsequently, a hierarchical merging stage compresses this pre-filtered set, effectively summarizing semantic content without the noise-induced feature dilution seen in single-stage methods. Extensive experiments on 29 VDR datasets demonstrate that our framework consistently outperforms existing methods, significantly extending the near-lossless compression range and providing robust performance at high compression ratios.
Executive Summary
The article introduces a novel two-stage framework, Prune-then-Merge, to improve the efficiency of multi-vector visual document retrieval. This framework first prunes low-information patches and then merges the remaining embeddings, resulting in a high-signal set of embeddings that preserves semantic content. The authors demonstrate the effectiveness of their approach through extensive experiments on 29 datasets, showing significant improvements in compression ratios and near-lossless compression ranges.
Key Points
- ▸ Introduction of the Prune-then-Merge framework
- ▸ Adaptive pruning stage to filter out low-information patches
- ▸ Hierarchical merging stage to compress pre-filtered embeddings
Merits
Improved Compression Efficiency
The Prune-then-Merge framework achieves higher compression ratios while preserving semantic content
Demerits
Computational Overhead
The two-stage framework may introduce additional computational overhead compared to single-stage methods
Expert Commentary
The Prune-then-Merge framework represents a significant advancement in multi-vector visual document retrieval, offering a more efficient and effective approach to preserving semantic content. The authors' use of adaptive pruning and hierarchical merging stages demonstrates a nuanced understanding of the trade-offs between compression rate and feature fidelity. However, further research is needed to fully explore the potential applications and limitations of this framework, particularly in relation to computational overhead and scalability.
Recommendations
- ✓ Further experimentation on larger and more diverse datasets to validate the framework's effectiveness
- ✓ Investigation into potential applications of the Prune-then-Merge framework in other multimodal retrieval domains
