@inproceedings{li-etal-2026-rgcc,
title = "{RGCC}: Retriever-Guided Context Compression for Efficient Vision{--}Language Alignment in Knowledge-Based {VQA}",
author = "Li, Qiankun and
Zhai, Feifei and
Chen, Jianghao and
Li, Chong and
Gao, Haoran and
Meng, Fanyu and
Feng, Junlan and
Zhao, Yang and
Zhou, Yu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.682/",
pages = "14313--14335",
ISBN = "979-8-89176-462-0",
abstract = "Knowledge-Based Visual Question Answering (KB-VQA) requires retrieving external textual knowledge to answer visual queries. However, current methods often suffer from two critical misalignments during retrieval: distributional misalignment, driven by the severe stylistic shift from descriptive pre-training image captions to formal knowledge texts, and granularity misalignment, where lengthy knowledge documents contain excessive noise that drowns out key information, making it difficult to accurately match the visual query. To resolve these challenges, we propose RGCC, a Retriever-Guided Context Compression framework. By mining alignment-friendly data and further training a context compression model, RGCC condenses lengthy documents into compact, retriever-preferred representations that significantly improve cross-modal retrieval. Extensive experiments on the InfoSeek and E-VQA benchmarks demonstrate that RGCC achieves state-of-the-art retrieval performance and boosts downstream question-answering accuracy without additional online inference overhead."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2026-rgcc">
<titleInfo>
<title>RGCC: Retriever-Guided Context Compression for Efficient Vision–Language Alignment in Knowledge-Based VQA</title>
</titleInfo>
<name type="personal">
<namePart type="given">Qiankun</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Feifei</namePart>
<namePart type="family">Zhai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jianghao</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chong</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haoran</namePart>
<namePart type="family">Gao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fanyu</namePart>
<namePart type="family">Meng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junlan</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Knowledge-Based Visual Question Answering (KB-VQA) requires retrieving external textual knowledge to answer visual queries. However, current methods often suffer from two critical misalignments during retrieval: distributional misalignment, driven by the severe stylistic shift from descriptive pre-training image captions to formal knowledge texts, and granularity misalignment, where lengthy knowledge documents contain excessive noise that drowns out key information, making it difficult to accurately match the visual query. To resolve these challenges, we propose RGCC, a Retriever-Guided Context Compression framework. By mining alignment-friendly data and further training a context compression model, RGCC condenses lengthy documents into compact, retriever-preferred representations that significantly improve cross-modal retrieval. Extensive experiments on the InfoSeek and E-VQA benchmarks demonstrate that RGCC achieves state-of-the-art retrieval performance and boosts downstream question-answering accuracy without additional online inference overhead.</abstract>
<identifier type="citekey">li-etal-2026-rgcc</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.682/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>14313</start>
<end>14335</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T RGCC: Retriever-Guided Context Compression for Efficient Vision–Language Alignment in Knowledge-Based VQA
%A Li, Qiankun
%A Zhai, Feifei
%A Chen, Jianghao
%A Li, Chong
%A Gao, Haoran
%A Meng, Fanyu
%A Feng, Junlan
%A Zhao, Yang
%A Zhou, Yu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-rgcc
%X Knowledge-Based Visual Question Answering (KB-VQA) requires retrieving external textual knowledge to answer visual queries. However, current methods often suffer from two critical misalignments during retrieval: distributional misalignment, driven by the severe stylistic shift from descriptive pre-training image captions to formal knowledge texts, and granularity misalignment, where lengthy knowledge documents contain excessive noise that drowns out key information, making it difficult to accurately match the visual query. To resolve these challenges, we propose RGCC, a Retriever-Guided Context Compression framework. By mining alignment-friendly data and further training a context compression model, RGCC condenses lengthy documents into compact, retriever-preferred representations that significantly improve cross-modal retrieval. Extensive experiments on the InfoSeek and E-VQA benchmarks demonstrate that RGCC achieves state-of-the-art retrieval performance and boosts downstream question-answering accuracy without additional online inference overhead.
%U https://aclanthology.org/2026.findings-emnlp.682/
%P 14313-14335
Markdown (Informal)
[RGCC: Retriever-Guided Context Compression for Efficient Vision–Language Alignment in Knowledge-Based VQA](https://aclanthology.org/2026.findings-emnlp.682/) (Li et al., Findings 2026)
ACL
- Qiankun Li, Feifei Zhai, Jianghao Chen, Chong Li, Haoran Gao, Fanyu Meng, Junlan Feng, Yang Zhao, and Yu Zhou. 2026. RGCC: Retriever-Guided Context Compression for Efficient Vision–Language Alignment in Knowledge-Based VQA. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 14313–14335, Budapest, Hungary. Association for Computational Linguistics.