AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation
_reachsumit · x · 2026-08-27
Paper proposes AdaptiveEmbed, challenging the fixed-vector paradigm in multimodal retrieval.
Core Methodology:
- Sample-Adaptive: Allocates a different number of embedding vectors per sample based on retrieval utility, rather than a fixed budget.
- CAES: Each sample is represented by a Content-Adaptive Embedding Set, sized by the utility of additional vectors.
- Training Framework: Uses Multi-Group Contrastive Learning (MGCL) and symmetric set-to-set similarity (SetSim) to learn structured representations, employing a utility policy to determine optimal vector counts.
This achieves a better trade-off between efficiency and accuracy.
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