@inproceedings{kim-etal-2026-sage,
title = "{SAGE}: Semantic Attribute Graphs for Multi-Entity Visual Retrieval",
author = "Kim, Yongjoo and
Kwon, Mincheol and
Choi, Seonga and
Lee, Minseung and
Oh, Kyeong-Jin and
Lee, Hyunyoung and
Choi, Yunsu and
Lee, Jungbeom",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2405/",
pages = "54643--54656",
ISBN = "979-8-89176-459-0",
abstract = "Dense document images often contain many fine-grained visual and textual entities whose relevance depends on a user query. Standard vision-language retrievers encode cropped regions with a single vector, which can mix distinct entity signals and obscure the evidence needed for fine-grained retrieval. We call this failure mode **Semantic Dilution** and quantitatively show that it degrades entity-level retrieval as a function of entity density. To mitigate it, we propose **SAGE**, a training-free framework that parses semantic entities from dense document images, represents them as hierarchical graph nodes with multi-vector embeddings, and retrieves query-relevant evidence through iterative entity-level subgraph matching. We also introduce **DEAR**, a dataset of 1,055 query{--}image pairs sourced from product detail pages, where each query requires retrieving and comparing multiple fine-grained entities from visually dense inputs across four question types of increasing complexity. Experiments show that SAGE substantially reduces semantic dilution and outperforms patch-level and OCR-based retrieval baselines on DEAR, achieving a Recall@3 of 0.849 and a generation score of 2.746 on multi-entity visual comparison queries. Our code is available at https://github.com/All4Nothing/SAGE."
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<abstract>Dense document images often contain many fine-grained visual and textual entities whose relevance depends on a user query. Standard vision-language retrievers encode cropped regions with a single vector, which can mix distinct entity signals and obscure the evidence needed for fine-grained retrieval. We call this failure mode **Semantic Dilution** and quantitatively show that it degrades entity-level retrieval as a function of entity density. To mitigate it, we propose **SAGE**, a training-free framework that parses semantic entities from dense document images, represents them as hierarchical graph nodes with multi-vector embeddings, and retrieves query-relevant evidence through iterative entity-level subgraph matching. We also introduce **DEAR**, a dataset of 1,055 query–image pairs sourced from product detail pages, where each query requires retrieving and comparing multiple fine-grained entities from visually dense inputs across four question types of increasing complexity. Experiments show that SAGE substantially reduces semantic dilution and outperforms patch-level and OCR-based retrieval baselines on DEAR, achieving a Recall@3 of 0.849 and a generation score of 2.746 on multi-entity visual comparison queries. Our code is available at https://github.com/All4Nothing/SAGE.</abstract>
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%0 Conference Proceedings
%T SAGE: Semantic Attribute Graphs for Multi-Entity Visual Retrieval
%A Kim, Yongjoo
%A Kwon, Mincheol
%A Choi, Seonga
%A Lee, Minseung
%A Oh, Kyeong-Jin
%A Lee, Hyunyoung
%A Choi, Yunsu
%A Lee, Jungbeom
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-sage
%X Dense document images often contain many fine-grained visual and textual entities whose relevance depends on a user query. Standard vision-language retrievers encode cropped regions with a single vector, which can mix distinct entity signals and obscure the evidence needed for fine-grained retrieval. We call this failure mode **Semantic Dilution** and quantitatively show that it degrades entity-level retrieval as a function of entity density. To mitigate it, we propose **SAGE**, a training-free framework that parses semantic entities from dense document images, represents them as hierarchical graph nodes with multi-vector embeddings, and retrieves query-relevant evidence through iterative entity-level subgraph matching. We also introduce **DEAR**, a dataset of 1,055 query–image pairs sourced from product detail pages, where each query requires retrieving and comparing multiple fine-grained entities from visually dense inputs across four question types of increasing complexity. Experiments show that SAGE substantially reduces semantic dilution and outperforms patch-level and OCR-based retrieval baselines on DEAR, achieving a Recall@3 of 0.849 and a generation score of 2.746 on multi-entity visual comparison queries. Our code is available at https://github.com/All4Nothing/SAGE.
%U https://aclanthology.org/2026.emnlp-main.2405/
%P 54643-54656
Markdown (Informal)
[SAGE: Semantic Attribute Graphs for Multi-Entity Visual Retrieval](https://aclanthology.org/2026.emnlp-main.2405/) (Kim et al., EMNLP 2026)
ACL
- Yongjoo Kim, Mincheol Kwon, Seonga Choi, Minseung Lee, Kyeong-Jin Oh, Hyunyoung Lee, Yunsu Choi, and Jungbeom Lee. 2026. SAGE: Semantic Attribute Graphs for Multi-Entity Visual Retrieval. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54643–54656, Budapest, Hungary. Association for Computational Linguistics.