| title | EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking |
|---|---|
| authors | Enjun Du, Siyi Liu, Zirong Chen, Xinyu Zuo, Jinwen Luo, Ruiwen Tao, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, Yongqi Zhang |
| date | 2026-08-21 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.20886 |
| upvotes | 11 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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 multifaceted 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. |
cite
@misc{du2026evirank,
title={EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking},
author={Enjun Du and Siyi Liu and Zirong Chen and Xinyu Zuo and Jinwen Luo and Ruiwen Tao and Lisheng Duan and Haijin Liang and Jin Ma and Junfu Pu and Yongqi Zhang},
year={2026},
eprint={2608.20886},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.20886}
}