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Found 5 papers, 0 papers with code
Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
RefinedFields: Radiance Fields Refinement for Unconstrained Scenes
We carry out extensive experiments and verify the merit of our method on synthetic data and real tourism photo collections.
Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrieval
Pick a training dataset, pick a backbone network for feature extraction, and voil\`a ; this usually works for a variety of use cases.
Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning
Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving rise to a variety of small datasets and associated dataset-specific methods.
Challenging deep image descriptors for retrieval in heterogeneous iconographic collections
This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view Permission to make digital
