DATA AND COMPUTILITY ISLANDS IN REMOTE SENSING FOR EO The rapid advancement of Earth observation(EO)capabilities is driving an explosive increase in remote sensing data.There is an urgent need for advanced processing ...DATA AND COMPUTILITY ISLANDS IN REMOTE SENSING FOR EO The rapid advancement of Earth observation(EO)capabilities is driving an explosive increase in remote sensing data.There is an urgent need for advanced processing techniques to unleash their application value.1 Generalist EO intelligence refers to the ability to provide unified support for qualitative interpretation,quantitative inversion,and interactive dialogue across diverse EO data and tasks.It has attracted significant attention recently,prompting academia,industry,and government to invest substantial resources.2 Through developing remote sensing foundation models(RSFMs),generalist EO intelligence can ultimately offer humanity a shared spatial-temporal intelligence service in various fields(e.g.,agriculture,forestry,and oceanography).3 However,a critical question remains:have we truly unleashed the potential of RSFMs for generalist EO intelligence?Despite the vast volume of remote sensing data,their distribution is often fragmented and decentralized due to privacy concerns,storage bottlenecks,industrial competition,and geo-information security.This fragmentation leads to data islands,which limit the full utilization of multi-source remote sensing data.Moreover,computility(i.e.,computational resources)typically develops in isolation,inadequately supporting the large-scale training and application of RSFMs.展开更多
In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly differen...In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains unknown.However,the performance of current DG ReID relies heavily on labor-intensive source domain annotations.Considering the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in ReID.Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain.To address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person ReID.This involves independently training multiple experts to account for label space inconsistencies between source domains.At the same time,the DaE captures domain-generalizable information for testing.Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting.The results demonstrate the superiority of our method over state-of-the-art techniques.We will make our code and models available for public use.展开更多
基金supported by the National Natural Science Foundation of China under grants 42030102 and 42371321 and by the Ant Group。
文摘DATA AND COMPUTILITY ISLANDS IN REMOTE SENSING FOR EO The rapid advancement of Earth observation(EO)capabilities is driving an explosive increase in remote sensing data.There is an urgent need for advanced processing techniques to unleash their application value.1 Generalist EO intelligence refers to the ability to provide unified support for qualitative interpretation,quantitative inversion,and interactive dialogue across diverse EO data and tasks.It has attracted significant attention recently,prompting academia,industry,and government to invest substantial resources.2 Through developing remote sensing foundation models(RSFMs),generalist EO intelligence can ultimately offer humanity a shared spatial-temporal intelligence service in various fields(e.g.,agriculture,forestry,and oceanography).3 However,a critical question remains:have we truly unleashed the potential of RSFMs for generalist EO intelligence?Despite the vast volume of remote sensing data,their distribution is often fragmented and decentralized due to privacy concerns,storage bottlenecks,industrial competition,and geo-information security.This fragmentation leads to data islands,which limit the full utilization of multi-source remote sensing data.Moreover,computility(i.e.,computational resources)typically develops in isolation,inadequately supporting the large-scale training and application of RSFMs.
基金supported by the National Natural Science Foundation of China(Nos.62225113,62176188,and 623B2080)the Innovative Research Group Project of Hubei Province(No.2024AFA017).
文摘In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains unknown.However,the performance of current DG ReID relies heavily on labor-intensive source domain annotations.Considering the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in ReID.Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain.To address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person ReID.This involves independently training multiple experts to account for label space inconsistencies between source domains.At the same time,the DaE captures domain-generalizable information for testing.Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting.The results demonstrate the superiority of our method over state-of-the-art techniques.We will make our code and models available for public use.
