As various types of data grow explosively,largescale data storage,backup,and transmission become challenging,which motivates many researchers to propose efficient universal compression algorithms for multi-source data...As various types of data grow explosively,largescale data storage,backup,and transmission become challenging,which motivates many researchers to propose efficient universal compression algorithms for multi-source data.In recent years,due to the emergence of hardware acceleration devices such as GPUs,TPUs,DPUs,and FPGAs,the performance bottleneck of neural networks(NN)has been overcome,making NN-based compression algorithms increasingly practical and popular.However,the research survey for the NN-based universal lossless compressors has not been conducted yet,and there is also a lack of unified evaluation metrics.To address the above problems,in this paper,we present a holistic survey as well as benchmark evaluations.Specifically,i)we thoroughly investigate NNbased lossless universal compression algorithms toward multisource data and classify them into 3 types:static pre-training,adaptive,and semi-adaptive.ii)We unify 19 evaluation metrics to comprehensively assess the compression effect,resource consumption,and model performance of compressors.iii)We conduct experiments more than 4600 CPU/GPU hours to evaluate 17 state-of-the-art compressors on 28 real-world datasets across data types of text,images,videos,audio,etc.iv)We also summarize the strengths and drawbacks of NNbased lossless data compressors and discuss promising research directions.We summarize the results as the NN-based Lossless Compressors Benchmark(NNLCB,See fahaihi.github.io/NNLCB website),which will be updated and maintained continuously in the future.展开更多
Recognizing dynamic variations on the ground,especially changes caused by various natural disasters,is critical for assessing the severity of the damage and directing the disaster response.However,current workflows fo...Recognizing dynamic variations on the ground,especially changes caused by various natural disasters,is critical for assessing the severity of the damage and directing the disaster response.However,current workflows for disaster assessment usually require human analysts to observe and identify damaged buildings,which is labor-intensive and unsuitable for large-scale disaster areas.In this paper,we propose a difference-aware attention network(D2ANet)for simultaneous building localization and multi-level change detection from the dual-temporal satellite imagery.Considering the differences in different channels in the features of pre-and post-disaster images,we develop a dual-temporal aggregation module using paired features to excite change-sensitive channels of the features and learn the global change pattern.Since the nature of building damage caused by disasters is diverse in complex environments,we design a difference-attention module to exploit local correlations among the multi-level changes,which improves the ability to identify damage on different scales.Extensive experiments on the large-scale building damage assessment dataset xBD demonstrate that our approach provides new state-of-the-art results.Source code is publicly available at https://github.com/mj129/D2ANet.展开更多
Physics-based fluid simulation has played an increasingly important role in the computer graphics community.Recent methods in this area have greatly improved the generation of complex visual effects and its computatio...Physics-based fluid simulation has played an increasingly important role in the computer graphics community.Recent methods in this area have greatly improved the generation of complex visual effects and its computational efficiency.Novel techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine details.The parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research perspectives.In this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last decade.Driven by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and control.From a practical perspective,we give an overview of existing implementations available for the above methods.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62272253 and 62272252)the Fundamental Research Funds for the Central Universities.It was also supported in part by the China Scholarship Council(CSC202406200085)the Innovation Project of Guangxi Graduate Education(YCBZ2024005).
文摘As various types of data grow explosively,largescale data storage,backup,and transmission become challenging,which motivates many researchers to propose efficient universal compression algorithms for multi-source data.In recent years,due to the emergence of hardware acceleration devices such as GPUs,TPUs,DPUs,and FPGAs,the performance bottleneck of neural networks(NN)has been overcome,making NN-based compression algorithms increasingly practical and popular.However,the research survey for the NN-based universal lossless compressors has not been conducted yet,and there is also a lack of unified evaluation metrics.To address the above problems,in this paper,we present a holistic survey as well as benchmark evaluations.Specifically,i)we thoroughly investigate NNbased lossless universal compression algorithms toward multisource data and classify them into 3 types:static pre-training,adaptive,and semi-adaptive.ii)We unify 19 evaluation metrics to comprehensively assess the compression effect,resource consumption,and model performance of compressors.iii)We conduct experiments more than 4600 CPU/GPU hours to evaluate 17 state-of-the-art compressors on 28 real-world datasets across data types of text,images,videos,audio,etc.iv)We also summarize the strengths and drawbacks of NNbased lossless data compressors and discuss promising research directions.We summarize the results as the NN-based Lossless Compressors Benchmark(NNLCB,See fahaihi.github.io/NNLCB website),which will be updated and maintained continuously in the future.
基金supported by the National Key R&D Program of China(Grant No.2018AAA0100400)Fundamental Research Funds for the Central Universities(Nankai University,Grant No.63223050)National Natural Science Foundation of China(Grant No.62176130).
文摘Recognizing dynamic variations on the ground,especially changes caused by various natural disasters,is critical for assessing the severity of the damage and directing the disaster response.However,current workflows for disaster assessment usually require human analysts to observe and identify damaged buildings,which is labor-intensive and unsuitable for large-scale disaster areas.In this paper,we propose a difference-aware attention network(D2ANet)for simultaneous building localization and multi-level change detection from the dual-temporal satellite imagery.Considering the differences in different channels in the features of pre-and post-disaster images,we develop a dual-temporal aggregation module using paired features to excite change-sensitive channels of the features and learn the global change pattern.Since the nature of building damage caused by disasters is diverse in complex environments,we design a difference-attention module to exploit local correlations among the multi-level changes,which improves the ability to identify damage on different scales.Extensive experiments on the large-scale building damage assessment dataset xBD demonstrate that our approach provides new state-of-the-art results.Source code is publicly available at https://github.com/mj129/D2ANet.
基金funded by National Key R&D Program of China(No.2022ZD0118001)National Natural Science Foundation of China(Nos.62376025 and 62332017)+1 种基金Horizon 2020-Marie SklodowskaCurie Action-Individual Fellowships(No.895941)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030177)。
文摘Physics-based fluid simulation has played an increasingly important role in the computer graphics community.Recent methods in this area have greatly improved the generation of complex visual effects and its computational efficiency.Novel techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine details.The parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research perspectives.In this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last decade.Driven by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and control.From a practical perspective,we give an overview of existing implementations available for the above methods.
