Who cites «MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval»
- Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge FrameworkFindings of the Association for Computational LinguisticsYibo Yan, Mingdong Ou, Yi Cao, et al.2026 · 2 citations
- DRAGON: Domain-specific Robust Automatic Data Generation for RAG OptimizationFindings of the Association for Computational LinguisticsHaiyang Shen, Hang Yan, Zhongshi Xing, et al.2026 · 1 citation
- Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Weiqing Luo, Zongye Hu, Xiao Wang, et al.2026 · 1 citation
- AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerceFindings of the Association for Computational LinguisticsBiao Zhang, Lixin Chen, Bin Zhang, et al.2026
- Benchmarking the Fine-Grained Discriminability in Image-Text Retrieval via Controlled Contrastive DifferencesFindings of the Association for Computational LinguisticsZhen Wang, Xi Zhou, Yating Yang, et al.2026
- Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image RetrievalFindings of the Association for Computational LinguisticsNan Sun, Jing Tang, Lei Sun, et al.2026
- Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Peiru Yang, Haoran Zheng, Tong Ju, et al.2026
- Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image EditingAnnual Meeting of the Association for Computational Linguistics (ACL)Tingyu Song, Yanzhao Zhang, Mingxin Li, et al.2026
This page in Markdown → https://md.research.elamur.ai/free/en/papers/zhou-2025-megapairs-massive-data-synthesis-universal/cited-by/