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 capacity, assigning the same number of vectors to all samples regardless of their individual retrieval demands. Such a fixed-capacity formulation overlooks the fact that different samples may require different amounts of representation capacity for effective retrieval. In this work, we introduce \emph{Sample-Adaptive Multi-Vector Representation} (SAMVR), a new problem setting for multimodal retrieval that studies how m...
| Authors | Xinze Liu, Lei Yang, Dayan Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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