For fans of Zhenya and Katya, these updates are a dream come true. The new content is packed with fresh poses, animations, and scenarios that showcase the models' versatility and charm. Whether you're a seasoned enthusiast or just discovering Vladmodels, there's never been a better time to dive into the world of 3D modeling and animation.
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| 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). | For fans of Zhenya and Katya, these updates