Papers with MMIR
DocMMIR: A Framework for Document Multi-modal Information Retrieval (2025.findings-emnlp)
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| Challenge: | Existing multi-modal information retrieval models lack a comprehensive exploration of document-level retrieval . existing models suffer from the absence of cross-domain datasets at this granularity. |
| Approach: | They propose a multi-modal document retrieval framework to unify diverse document formats and domains with a comprehensive retrieval scenario. |
| Outcome: | The proposed framework improves document retrieval performance on a large multimodal dataset. |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
Zero-Shot Multimodal Retrieval with Multi-Scale Contextual Representations (2026.acl-long)
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| Challenge: | Existing approaches to multimodal information retrieval (MMIR) lack generalization across different modalities and require annotated training data. |
| Approach: | They propose a fine-tuning-free, two-stage MMIR approach that couples efficient candidate filtering with fine-grained multimodal re-ranking. |
| Outcome: | The proposed approach outperforms supervised methods on 23 datasets. |
Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models (2025.findings-acl)
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| Challenge: | Existing Multimodal Large Language Models (MLLMs) are predominantly trained on consistent visual-textual inputs, leaving open the question of whether they can handle semantic mismatches in layout-rich content. |
| Approach: | They propose to use multimodal inconsistency reasoning to assess MLLMs' ability to reason about semantic mismatches in webpages, presentation slides, and posters. |
| Outcome: | The proposed model outperforms open-source models in detecting inconsistencies in webpages, presentation slides, and posters while remaining vulnerable to inconsistent errors. |