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New research explores efficient multimodal retrieval with adaptive embeddings

By PulseAugur Editorial · [4 sources] ·

Three new research papers explore advancements in multimodal retrieval, focusing on improving efficiency and performance. The first paper introduces ReT-2, a unified retrieval model using a recurrent Transformer architecture with LSTM-inspired gating for multimodal queries and documents, achieving state-of-the-art results on M2KR and M-BEIR benchmarks. The second paper, PUMA, presents a post-hoc sparsification method for universal multimodal embeddings, significantly reducing storage and inference costs without retraining the backbone, showing competitive results on various benchmarks. The third paper, AdaptiveEmbed, proposes a sample-adaptive multi-vector representation approach, allowing variable embedding capacity per sample to enhance retrieval performance across different modalities. AI

IMPACT These advancements in multimodal retrieval could lead to more efficient and powerful AI systems capable of understanding and processing diverse data types.

RANK_REASON Three academic papers published on arXiv detailing new methods for multimodal retrieval.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New research explores efficient multimodal retrieval with adaptive embeddings

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COVERAGE [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: Post-Hoc Sparsification of Universal Multimodal Embeddings for Efficient Retrieval

    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: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval

    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: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval

    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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