🔍 Information Retrieval & RAG¶

🎞️ ECCV2026 · 2 paper notes

HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding: HyFL-CLIP distills the Euclidean image-text alignment of pretrained CLIP into hyperbolic space (Lorentz model). By modeling the "global description - local component" hierarchical entailment relationship via Einstein midpoint aggregation, the model stably retrieves correct images even when long texts are perturbed (reordered, deleted, word-dropped), achieving up to a 19.5% improvement over the strongest baseline under word perturbations.
LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement: To address the issue where MLLMs are too slow to encode all pages in visual document retrieval, LightSTAR proposes first filtering out candidates with high recall using an LLM-free lightweight visual selection module, and then performing vision-adaptive semantic refinement only on those candidates. This achieves 89.1 NDCG@5 on ViDoRe while reducing the end-to-end latency for 5,000 pages to 123.9s.