ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval
ConceptFormer enhances visual document retrieval by learning adaptive latent concepts, achieving a 16.7% NDCG@10 improvement.
Key Findings
Methodology
ConceptFormer employs a latent concept representation learning framework, modeling query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance. It dynamically determines the number of latent concept tokens using a strong vision-language model and uses these concepts as an intermediate representation to bridge the semantic gap between queries and documents.
Key Results
- On diverse visual document retrieval benchmarks, ConceptFormer achieves 16.7% and 22.1% relative improvements in NDCG@10 over the strongest visual retrieval and OCR-based text retrieval baselines, respectively.
- Experiments reveal that latent concepts effectively connect localized visual evidence with semantic relevance, enabling the retriever to capture fine-grained textual cues and complex document-level visual structures.
- Ablation studies show that the introduction of latent concepts significantly enhances retrieval alignment.
Significance
ConceptFormer is significant in the field of visual document retrieval, addressing limitations in handling complex visual structures and semantic relevance. By introducing latent concepts, it provides finer-grained supervision signals, improving retrieval accuracy and robustness. This method impacts both academia and industry by offering new insights into multimodal information retrieval.
Technical Contribution
ConceptFormer's technical contribution lies in introducing adaptive latent concepts, dynamically adjusting concept capacity to suit different query-document pairs. This approach differs from existing global matching methods, providing finer-grained supervision signals and enhancing visual document representation capabilities.
Novelty
ConceptFormer is the first to introduce latent concepts into visual document retrieval, offering a new mechanism for modeling query-relevant evidence. Unlike existing methods, it does not rely on textual intermediate representations or direct reliance on raw visual annotations.
Limitations
- In handling extremely complex document structures, the capacity of latent concepts may be insufficient to capture all relevant information.
- The method heavily relies on the performance of the vision-language model, which may be limited by the quality of pre-training.
Future Work
Future research directions include exploring more efficient latent concept learning methods and validating ConceptFormer's performance on larger datasets. Additionally, applying this method to other multimodal retrieval tasks could be investigated.
AI Executive Summary
Visual document retrieval is a critical component of multimodal retrieval-augmented generation, aiming to identify query-relevant pages from document collections. Traditional methods often rely on textual descriptions or localized visual regions as evidence proxies, which may overlook complex visual structures or provide incomplete evidence representations.
To address these issues, ConceptFormer proposes a latent concept representation learning framework, modeling query-relevant evidence as continuous, query-conditioned latent concepts. This approach does not require textual intermediate representations or direct reliance on raw visual annotations. Instead, it uses a strong vision-language model to dynamically determine the number of latent concept tokens and employs these concepts as an intermediate representation to bridge the semantic gap between queries and documents.
Experimental results demonstrate that ConceptFormer achieves significant performance improvements on diverse visual document retrieval benchmarks, particularly with a 16.7% and 22.1% improvement in NDCG@10 over the strongest baselines. This method effectively connects localized visual evidence with semantic relevance, enabling the retriever to capture fine-grained textual cues and complex document-level visual structures.
Deep Analysis
Background
Visual document retrieval has gained significant attention as a fundamental component of multimodal document understanding. Traditional methods often rely on OCR to convert page images into textual representations, which can introduce recognition errors and lose essential visual information. Recent research has explored direct retrieval from page images to overcome these limitations.
Core Problem
Existing visual document retrieval systems struggle to capture fine-grained query-document relevance, often providing only coarse-grained relevance signals, making it challenging to identify critical fine-grained cues.
Innovation
ConceptFormer's core innovation lies in introducing adaptive latent concepts, dynamically adjusting concept capacity to suit different query-document pairs. This approach provides finer-grained supervision signals, enhancing visual document representation capabilities.
Methodology
- �� Use a strong vision-language model to dynamically determine the number of latent concept tokens.
- �� Employ these concepts as intermediate representations to bridge the semantic gap between queries and documents.
- �� Introduce latent concepts to provide finer-grained supervision signals.
Experiments
Experiments are conducted on multiple visual document retrieval benchmarks, including InfoVQA and ChartQA. NDCG@10 is used as the primary evaluation metric, with comparisons to the strongest visual retrieval and OCR-based text retrieval baselines.
Results
Results show that ConceptFormer achieves a 16.7% and 22.1% improvement in NDCG@10 over the strongest baselines. Ablation studies demonstrate that the introduction of latent concepts significantly enhances retrieval alignment.
Applications
ConceptFormer can be applied to multimodal information retrieval tasks, such as industrial documents, infographics, and web page retrieval. Its fine-grained supervision signals help improve retrieval accuracy.
Limitations & Outlook
While ConceptFormer performs well on multiple benchmarks, its capacity may be insufficient to capture all relevant information in extremely complex document structures. Additionally, the method heavily relies on the performance of the vision-language model.
Plain Language Accessible to non-experts
Imagine you're in a library searching for a book. Traditional methods are like finding it by title or author, while ConceptFormer is like pinpointing the specific chapters or paragraphs relevant to your query. This approach not only focuses on the overall information of the book but also identifies the parts most relevant to the query, enhancing retrieval accuracy.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a treasure hunt game. Traditional methods are like finding the treasure using a rough map, while ConceptFormer is like using specific clues to get closer to the treasure step by step. It helps you find those well-hidden treasures!
Glossary
Visual Document Retrieval
Retrieving query-relevant pages from visually rich document collections.
ConceptFormer is used to enhance the accuracy of visual document retrieval.
Latent Concept
A continuous, query-conditioned representation that bridges localized visual evidence and semantic relevance.
ConceptFormer uses latent concepts to bridge the semantic gap between queries and documents.
Vision-Language Model
A model capable of processing both visual and language information.
ConceptFormer uses a vision-language model to dynamically determine the number of latent concepts.
NDCG@10
A metric for evaluating information retrieval system performance, considering relevance and ranking.
ConceptFormer achieves significant improvements in NDCG@10.
Contrastive Learning
A method of learning representations by contrasting positive and negative samples.
ConceptFormer uses contrastive learning to optimize the retriever.
Open Questions Unanswered questions from this research
- 1 How to effectively apply latent concepts in extremely complex document structures?
- 2 How does the capacity of latent concepts dynamically adapt to different query-document pairs?
Applications
Immediate Applications
Information Retrieval
ConceptFormer can be used to improve the accuracy of multimodal information retrieval, especially in industrial documents and infographics.
Long-term Vision
Intelligent Document Analysis
By further optimizing latent concept learning, ConceptFormer has the potential to play a greater role in intelligent document analysis.
Abstract
Visual document retrieval is a critical component of multimodal retrieval-augmented generation, aiming to identify query-relevant pages from document collections where evidence is distributed across text, layout, charts, and visual structures. Recent efforts toward finer-grained supervision primarily rely on textual descriptions or localized visual regions as evidence proxies. However, such supervision signals may either overlook complex visual structures or provide incomplete and inaccurate representations of the underlying evidence. To address these limitations, we propose ConceptFormer, a latent concept representation learning framework for visual document retrieval. ConceptFormer models query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance, without requiring either textual intermediate representations or direct reliance on raw visual annotations. During training, ConceptFormer employs a strong vision-language model to dynamically determine the number of latent concept tokens and uses these concepts as an intermediate representation to bridge the semantic gap between queries and documents, thereby guiding the learning of the embedding space. Experiments on diverse visual document retrieval benchmarks demonstrate that ConceptFormer achieves 16.7\% and 22.1\% relative improvements in average NDCG@10 over the strongest visual retrieval baseline and the strongest OCR-based text retrieval baseline, respectively. Further analysis reveals that latent concepts effectively connect localized visual evidence with semantic relevance, enabling the retriever to capture both fine-grained textual cues and complex document-level visual structures while preserving strong retrieval alignment. Codes and data are available at https://github.com/Neuir/ConceptFormer.