@inproceedings{du-etal-2026-evirank,
title = "{E}vi{R}ank: Structured Relevance Evidence for Multimodal Image Re-ranking",
author = "Du, Enjun and
Liu, Siyi and
Chen, Zirong and
Zuo, Xinyu and
Luo, Jinwen and
Tao, Ruiwen and
Duan, Lisheng and
Liang, Haijin and
Ma, Jin and
Pu, Junfu and
Zhang, Yongqi",
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.332/",
pages = "7640--7666",
ISBN = "979-8-89176-459-0",
abstract = "Real-world image search queries are multimodal and compositional: ``find this shirt in pink'' specifies an entity to retain, an attribute to modify, and context to ignore. Yet existing re-rankers either compress such multi-faceted relevance into an opaque embedding or rely on free-form chain-of-thought that easily omits or hallucinates fine-grained constraints. Drawing on rubric- and checklist-based evaluation from NLP, we recast multimodal image re-ranking as a \textbf{semantic constraint satisfaction} problem and propose \textbf{EviRank}, which parses any query{---}text-only, image-only, or composed{---}into a unified \textit{evidence package}: typed criteria across six semantic slots (e.g., entities, attributes, relations), each labelled \textit{required}, \textit{forbidden}, or \textit{ignorable}. Re-ranking then reduces to \textbf{evidence-conditioned verification}, combining deterministic rubric scoring and evidence-grounded listwise comparison in a single training-free procedure. The explicit evidence can further serve as structured supervision for optionally distilling a lightweight student. Across five benchmarks spanning text-to-image, image-to-image, and composed image retrieval, EviRank achieves state-of-the-art performance, and the distilled student preserves over 90{\%} of the teacher{'}s capability at substantially lower cost."
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<abstract>Real-world image search queries are multimodal and compositional: “find this shirt in pink” specifies an entity to retain, an attribute to modify, and context to ignore. Yet existing re-rankers either compress such multi-faceted relevance into an opaque embedding or rely on free-form chain-of-thought that easily omits or hallucinates fine-grained constraints. Drawing on rubric- and checklist-based evaluation from NLP, we recast multimodal image re-ranking as a semantic constraint satisfaction problem and propose EviRank, which parses any query—text-only, image-only, or composed—into a unified evidence package: typed criteria across six semantic slots (e.g., entities, attributes, relations), each labelled required, forbidden, or ignorable. Re-ranking then reduces to evidence-conditioned verification, combining deterministic rubric scoring and evidence-grounded listwise comparison in a single training-free procedure. The explicit evidence can further serve as structured supervision for optionally distilling a lightweight student. Across five benchmarks spanning text-to-image, image-to-image, and composed image retrieval, EviRank achieves state-of-the-art performance, and the distilled student preserves over 90% of the teacher’s capability at substantially lower cost.</abstract>
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%0 Conference Proceedings
%T EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking
%A Du, Enjun
%A Liu, Siyi
%A Chen, Zirong
%A Zuo, Xinyu
%A Luo, Jinwen
%A Tao, Ruiwen
%A Duan, Lisheng
%A Liang, Haijin
%A Ma, Jin
%A Pu, Junfu
%A Zhang, Yongqi
%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 du-etal-2026-evirank
%X Real-world image search queries are multimodal and compositional: “find this shirt in pink” specifies an entity to retain, an attribute to modify, and context to ignore. Yet existing re-rankers either compress such multi-faceted relevance into an opaque embedding or rely on free-form chain-of-thought that easily omits or hallucinates fine-grained constraints. Drawing on rubric- and checklist-based evaluation from NLP, we recast multimodal image re-ranking as a semantic constraint satisfaction problem and propose EviRank, which parses any query—text-only, image-only, or composed—into a unified evidence package: typed criteria across six semantic slots (e.g., entities, attributes, relations), each labelled required, forbidden, or ignorable. Re-ranking then reduces to evidence-conditioned verification, combining deterministic rubric scoring and evidence-grounded listwise comparison in a single training-free procedure. The explicit evidence can further serve as structured supervision for optionally distilling a lightweight student. Across five benchmarks spanning text-to-image, image-to-image, and composed image retrieval, EviRank achieves state-of-the-art performance, and the distilled student preserves over 90% of the teacher’s capability at substantially lower cost.
%U https://aclanthology.org/2026.emnlp-main.332/
%P 7640-7666
Markdown (Informal)
[EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking](https://aclanthology.org/2026.emnlp-main.332/) (Du et al., EMNLP 2026)
ACL
- Enjun Du, Siyi Liu, Zirong Chen, Xinyu Zuo, Jinwen Luo, Ruiwen Tao, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, and Yongqi Zhang. 2026. EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 7640–7666, Budapest, Hungary. Association for Computational Linguistics.