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pro vyhledávání: '"Liu, Enci"'
Autor:
Liu, Enci, Meng, Chenlin, Kolodner, Matthew, Sung, Eun Jee, Chen, Sihang, Burke, Marshall, Lobell, David, Ermon, Stefano
Building coverage statistics provide crucial insights into the urbanization, infrastructure, and poverty level of a region, facilitating efforts towards alleviating poverty, building sustainable cities, and allocating infrastructure investments and p
Externí odkaz:
http://arxiv.org/abs/2301.01449
Publikováno v:
In Mechanical Systems and Signal Processing 1 October 2024 219
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling
Autor:
Meng, Chenlin, Liu, Enci, Neiswanger, Willie, Song, Jiaming, Burke, Marshall, Lobell, David, Ermon, Stefano
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geogr
Externí odkaz:
http://arxiv.org/abs/2112.09126
Language models (LMs) have made remarkable progress, but still struggle to generalize beyond the training data to rare linguistic patterns. Since rare entities and facts are prevalent in the queries users submit to popular applications such as search
Externí odkaz:
http://arxiv.org/abs/2110.08430
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling
Autor:
Meng, Chenlin, Liu, Enci, Neiswanger, Willie, Song, Jiaming, Burke, Marshall, Lobell, David, Ermon, Stefano
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:12034-12042
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitoring applications. However, performing object detection over large geogr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de5894e8d5b7039fd0114c814d527cd8
https://doi.org/10.1609/aaai.v36i11.21462
https://doi.org/10.1609/aaai.v36i11.21462
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