Vladmodels Zhenya Y114 Katya Y117 15 Upd [verified] Jun 2026

| Aspect | What you’ll get | |--------|-----------------| | | Introduces the VLAD pooling layer as a differentiable module that can be inserted into any CNN, turning the whole pipeline into an end‑to‑end trainable network. | | Implementation details | Provides the exact formulation of the “soft‑assignment” and the “intra‑normalisation + L2‑normalisation” steps that are now standard in all VLAD‑based pipelines. | | Training regime | Shows how to use weak GPS/geo‑tag supervision (triplet loss) to learn both the CNN backbone and the VLAD codebook simultaneously. | | Benchmarks | State‑of‑the‑art results on Pittsburgh, Tokyo 24/7, and Oxford/Paris retrieval datasets (the “15‑upd” benchmark you hinted at). |

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Both papers cite NetVLAD as the base and add (hard‑negative mining, multi‑scale pooling, and curriculum learning) that were codified in the internal “15‑upd” evaluation protocol used by many labs (including the one you referenced).

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