A unified moment-alignment theory bounds target-domain error by cross-domain differences in loss derivatives, and the new CMA algorithm implements exact gradient and Hessian matching in closed form.
Functional Map of the World
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abstract
We present a new dataset, Functional Map of the World (fMoW), which aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features. The metadata provided with each image enables reasoning about location, time, sun angles, physical sizes, and other features when making predictions about objects in the image. Our dataset consists of over 1 million images from over 200 countries. For each image, we provide at least one bounding box annotation containing one of 63 categories, including a "false detection" category. We present an analysis of the dataset along with baseline approaches that reason about metadata and temporal views. Our data, code, and pretrained models have been made publicly available.
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cs.LG 1years
2025 1verdicts
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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization
A unified moment-alignment theory bounds target-domain error by cross-domain differences in loss derivatives, and the new CMA algorithm implements exact gradient and Hessian matching in closed form.