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ConceptFormer framework improves visual document retrieval using latent concepts

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced ConceptFormer, a novel framework designed to enhance visual document retrieval. This approach learns adaptive latent concepts to better align queries with relevant documents, even when evidence is spread across text, layout, and visual structures. Unlike previous methods that relied on textual descriptions or raw visual annotations, ConceptFormer uses query-conditioned latent concepts as an intermediate representation. Experiments show ConceptFormer significantly improves retrieval accuracy, achieving substantial gains in NDCG@10 over existing visual and OCR-based baselines. AI

IMPACT This framework could enhance the accuracy of multimodal retrieval systems, improving how users find relevant information within complex documents.

RANK_REASON The cluster describes a research paper detailing a new framework for visual document retrieval.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

ConceptFormer framework improves visual document retrieval using latent concepts

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COVERAGE [3]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sun Maosong ·

    ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval

    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 …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval

    ConceptFormer learns continuous latent concept representations to bridge visual evidence and semantic relevance for visual document retrieval without relying on text intermediates or raw visual annotations.

  3. arXiv cs.CV TIER_1 English(EN) · Peng Chunyi, Xu Zhipeng, Yan Yukun, Liu Zhenghao, Yu Shi, Mei Sen, Sun Yubo, Zhang Yongheng, Zhou Jie, Gu Yu, Yu Ge, Sun Maosong ·

    ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval

    arXiv:2608.15698v1 Announce Type: new 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 …

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