Full Professor, Hefei University of Technology
2 papers at NeurIPS 2025
We introduce GARE, a gap-aware retrieval framework that learns pair-specific increments to alleviate optimization tension and false-negative noise in cross-modal alignment, achieving better uniformity and semantic structure.
