RQ-MoE combines two-level MoE with dual-stream quantization to create input-dependent codebooks that recover prior methods as special cases and deliver 6-14x faster decoding with on-par reconstruction and retrieval performance.
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GOMA refines frozen multimodal embeddings via modality-aware graph signal smoothing on attributed graphs to improve retrieval while avoiding over-smoothing.
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RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
RQ-MoE combines two-level MoE with dual-stream quantization to create input-dependent codebooks that recover prior methods as special cases and deliver 6-14x faster decoding with on-par reconstruction and retrieval performance.
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GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
GOMA refines frozen multimodal embeddings via modality-aware graph signal smoothing on attributed graphs to improve retrieval while avoiding over-smoothing.