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- W4214771064 abstract "For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component. Directly regressing camera pose/3D scene coordinates from the input image using deep neural networks has shown great potential. However, such methods assume a stationary data distribution with all scenes simultaneously available during training. In this paper, we approach the problem of visual localization in a continual learning setup – whereby the model is trained on scenes in an incremental manner. Our results show that similar to the classification domain, non-stationary data induces catastrophic forgetting in deep networks for visual localization. To address this issue, a strong baseline based on storing and replaying images from a fixed buffer is proposed. Furthermore, we propose a new sampling method based on coverage score (Buff-CS) that adapts the existing sampling strategies in the buffering process to the problem of visual localization. Results demonstrate consistent improvements over standard buffering methods on two challenging datasets – 7Scenes, 12Scenes, and also 19Scenes by combining the former scenes <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> ." @default.
- W4214771064 created "2022-03-02" @default.
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- W4214771064 date "2021-10-01" @default.
- W4214771064 modified "2023-09-29" @default.
- W4214771064 title "Continual Learning for Image-Based Camera Localization" @default.
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- W4214771064 doi "https://doi.org/10.1109/iccv48922.2021.00324" @default.
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