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English(EN) AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval

新研究探索高效多模态检索与自适应嵌入

作者 PulseAugur 编辑部 · [4 个来源] ·

三篇新研究论文探讨了多模态检索的进展，重点在于提高效率和性能。第一篇论文介绍了ReT-2，一个使用循环Transformer架构和受LSTM启发的门控机制的统一检索模型，用于多模态查询和文档，在M2KR和M-BEIR基准测试中取得了最先进的成果。第二篇论文PUMA提出了一种用于通用多模态嵌入的后验稀疏化方法，在不重新训练骨干模型的情况下显著降低了存储和推理成本，并在各种基准测试中取得了有竞争力的结果。第三篇论文AdaptiveEmbed提出了一种样本自适应多向量表示方法，允许每个样本具有可变的嵌入容量，以提高跨不同模态的检索性能。 AI

影响 多模态检索的这些进展可能导致更高效、更强大的AI系统，能够理解和处理多样化的数据类型。

排序理由 三篇在arXiv上发表的学术论文，详细介绍了多模态检索的新方法。

在 Hugging Face Daily Papers 阅读 →

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新研究探索高效多模态检索与自适应嵌入

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三篇在arXiv上发表的学术论文，详细介绍了多模态检索的新方法。
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报道来源 [4]

  1. arXiv cs.CL TIER_1 English(EN) · Davide Caffagni, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara ·

    Recurrence Meets Transformers for Universal Multimodal Retrieval

    arXiv:2509.08897v2 Announce Type: replace-cross Abstract: With the rapid advancement of multimodal retrieval and its application in LLMs and multimodal LLMs, increasingly complex retrieval tasks have emerged. Existing methods predominantly rely on task-specific fine-tuning of vis…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Tommaso Di Noia ·

    PUMA：通用多模态嵌入的后验稀疏化，用于高效检索

    Universal multimodal embedders enable retrieval across text, image, and combined queries, but their dense representations incur high memory and inference costs. Post-hoc sparsification could reduce these costs but remains underexplored for multimodal retrieval. We introduce PUMA,…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    AdaptiveEmbed：面向多模态检索的样本自适应多向量表示

    Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embeddings to capture fine-grained cross-modal information. However, existing approaches typically employ a fixed representation capac…

  4. arXiv cs.CV TIER_1 English(EN) · Xinze Liu, Lei Yang, Dayan Wu, Hengjie Zhu, Zihao Zhang, Hanqi Wu, Tianzhu Hu, Peng Fu, Zheng Lin, Weiping Wang ·

    AdaptiveEmbed：面向多模态检索的样本自适应多向量表示

    arXiv:2608.25412v1 Announce Type: new Abstract: Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embeddings to capture fine-grained cross-modal information. However, existing approach…

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