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
Class-incremental learning studies the problem of continually learning new classes from data streams.But networks suffer from catastrophic forgetting problems,forgetting past knowledge when acquiring new knowledge.Amo...Class-incremental learning studies the problem of continually learning new classes from data streams.But networks suffer from catastrophic forgetting problems,forgetting past knowledge when acquiring new knowledge.Among different approaches,replay methods have shown exceptional promise for this challenge.But performance still baffles from two aspects:(i)data in imbalanced distribution and(ii)networks with semantic inconsistency.First,due to limited memory buffer,there exists imbalance between old and new classes.Direct optimisation would lead feature space skewed towards new classes,resulting in performance degradation on old classes.Second,existing methods normally leverage previous network to regularise the present network.However,the previous network is not trained on new classes,which means that these two networks are semantic inconsistent,leading to misleading guidance information.To address these two problems,we propose BCSD(BiaMix contrastive learning and memory similarity distillation).For imbalanced distribution,we design Biased MixUp,where mixed samples are in high weight from old classes and low weight from new classes.Thus,network learns to push decision boundaries towards new classes.We further leverage label information to construct contrastive learning in order to ensure discriminability.Meanwhile,for semantic inconsistency,we distill knowledge from the previous network by capturing the similarity of new classes in current tasks to old classes from the memory buffer and transfer that knowledge to the present network.Empirical results on various datasets demonstrate its effectiveness and efficiency.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(62176188).
文摘Class-incremental learning studies the problem of continually learning new classes from data streams.But networks suffer from catastrophic forgetting problems,forgetting past knowledge when acquiring new knowledge.Among different approaches,replay methods have shown exceptional promise for this challenge.But performance still baffles from two aspects:(i)data in imbalanced distribution and(ii)networks with semantic inconsistency.First,due to limited memory buffer,there exists imbalance between old and new classes.Direct optimisation would lead feature space skewed towards new classes,resulting in performance degradation on old classes.Second,existing methods normally leverage previous network to regularise the present network.However,the previous network is not trained on new classes,which means that these two networks are semantic inconsistent,leading to misleading guidance information.To address these two problems,we propose BCSD(BiaMix contrastive learning and memory similarity distillation).For imbalanced distribution,we design Biased MixUp,where mixed samples are in high weight from old classes and low weight from new classes.Thus,network learns to push decision boundaries towards new classes.We further leverage label information to construct contrastive learning in order to ensure discriminability.Meanwhile,for semantic inconsistency,we distill knowledge from the previous network by capturing the similarity of new classes in current tasks to old classes from the memory buffer and transfer that knowledge to the present network.Empirical results on various datasets demonstrate its effectiveness and efficiency.
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.
