面向海洋的多模态智能计算：挑战、进展和展望

聂婕; 左子杰; 黄磊; 王志刚; 孙正雅; 仲国强; 王鑫; 王玉成; 刘安安; 张弘; 董军宇; 魏志强

doi:10.11834/jig.211267

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面向海洋的多模态智能计算：挑战、进展和展望
Marine oriented multimodal intelligent computing: challenges, progress and prospects
2022年27卷第9期页码：2589-2610
收稿：2021-12-31，

修回：2022-5-20，

录用：2022-5-27，

纸质出版：2022-09-16
DOI： 10.11834/jig.211267
稿件说明：

移动端阅览

聂婕, 左子杰, 黄磊, 王志刚, 孙正雅, 仲国强, 王鑫, 王玉成, 刘安安, 张弘, 董军宇, 魏志强. 面向海洋的多模态智能计算：挑战、进展和展望[J]. 中国图象图形学报, 2022,27(9):2589-2610. DOI： 10.11834/jig.211267.

Jie Nie, Zijie Zuo, Lei Huang, Zhigang Wang, Zhengya Sun, Guoqiang Zhong, Xin Wang, Yucheng Wang, An'an Liu, Hong Zhang, Junyu Dong, Zhiqiang Wei. Marine oriented multimodal intelligent computing: challenges, progress and prospects[J]. Journal of Image and Graphics, 2022, 27(9): 2589-2610. DOI： 10.11834/jig.211267.

摘要

海洋是高质量发展的要地，海洋科学大数据的发展为认知和经略海洋带来机遇的同时也引入了新的挑战。海洋科学大数据具有超多模态的显著特征，目前尚未形成面向海洋领域特色的多模态智能计算理论体系和技术框架。因此，本文首次从多模态数据技术的视角，系统性介绍面向海洋现象/过程的智能感知、认知和预知的交叉研究进展。首先，通过梳理海洋科学大数据全生命周期的阶段演进过程，明确海洋多模态智能计算的研究对象、科学问题和典型应用场景。其次，在海洋多模态大数据内容分析、推理预测和高性能计算3个典型应用场景中展开现有工作的系统性梳理和介绍。最后，针对海洋数据分布和计算模式的差异性，提出海洋多模态大数据表征建模、跨模态关联、推理预测以及高性能计算4个关键科学问题中的挑战，并提出未来展望。

Abstract

The marine-oriented research is essential to high-quality of human-based development. But

the current recognition of the ocean system is less than 5%. To understand the ocean

big marine data is acquired from observation

monitoring

investigation and statistics. Thanks to the development of the multi-scaled ocean observation system

the extensive of multi-modal marine oriented data has developed via remote sensing image

spatio-temporal analysis

simulation data

literature review and video & audio monitoring. To resilient the sustainable development of human society

current deep analysis and multimodal ocean data mining method has promoted the marine understanding on the aspects of ocean dynamic processes

energy and material cycles

the evolution of blue life

scientific discovery

healthy environment

and the quick response of extreme weather and climate change. Compared to traditional big data

the multi-modal big ocean data has its unique features

such as the super-giant system (covering 71% of the earth's surface

daily increment (10 TB)

super multi-perspectives ("land-sea-air-ice-earth based" coupling

"hydrometeorological-acoustical-optical and electromagnetic-based" polymorphism)

super spatial scale ("centimeter to hundreds kilometer based")

and temporal scale ("micro-second to inter-decadal based"). These features-derived challenges of existing multi-modal intelligent computing technology have to deal with such problems as cross-scale and multi-modal fusion analyses

multi-disciplinary and multi-domain coordinated reasoning

large computing power based multi-architecture compatible applications. We systematically introduce the cross-cutting researches of intelligent perception

cognition

and prediction for marine phenomena/processes based on multimodal data technology. First

we clarify the research objects

scientific problems

and typical application scenarios of marine multimodal intelligent computing through the evolution analysis of the lifecycle of marine science big data. Next

we target the differences between ocean data distribution and calculation patterns. We illustrate the uniqueness and scientific challenges of multimodal big marine data on the basis of modeling description

cross-modal correlation

inference prediction

and high-performance computing. 1) To bridge the "task gap" between big data and specific tasks for modeling description

we focus on effective feature extraction for related tasks of causality

differentiation

significance and robustness. The ocean-oriented differences and challenges are mainly discussed from six aspects including dynamic changes of physical structure

complex environmental noise

large intra-class differences

lack of reliable labels

unbalanced samples

and less public datasets. 2) To construct multi-circle layer

multi-scale and multi-perspective heterogeneous data

the cross-modal correlation modeling is obtained for reasonable integration of multi-model

effective reasoning of cross-model

and the multi-modalities of "heterogeneous gap bridging" through task matching

semantic consistency

and spatio-temporal correlation. The ocean field issue is mainly affected by four aspects of uneven data

large scale span

strong constraints of temporal and spatial

and high correlation of dimensions. 3) To fill the "unknown gap" of spatio-temporal information loss in the evolution of ocean

the reasoning and prediction requires the prior knowledge

experience

and reasoning ability in the field of modeling. The main differences of ocean fields are reflected in the three issues of dynamic evolution

spatio-temporal heterogeneity

and non-independent samples. 4) To reduce the "computing gap" between complex computing and real-time online analysis of marine super-giant systems

it is necessary to deal with the huge amount of data challenges in high-performance computing problems like increased resolution and the ocean processes refinement of online response analysis. In addition

we sort out and introduce existing work of typical application scenarios

such as marine multimedia content analysis

visual analysis

big data prediction

and high-performance computing. 1) Multimedia content analysis: we compare the technical features of existing marine research methods on the five aspects of target recognition

target re-identification

target retrieval

phenomenon/process recognition

and open datasets. 2) Visual analysis of marine big data: we summarize the matching issues of dynamic changes of physical structure

high correlation dimensions

and large-scale spans from the perspective of visualization

visualization analysis

and visualization system. 3) Ocean multimodal big data reasoning prediction: we review the existing research work from the perspectives of data-driven prediction and prediction of marine environmental variables

construction of marine knowledge graph

and knowledge reasoning. 4) High-performance computing issues of ocean multi-modal big data: we introduce and compare the relevant work on the three perspectives of memory-computing collaboration

multi-model acceleration

and giant system evaluation. Finally

we predict the ocean multimodal intelligent computing issues to be resolved and the future direction of it.

关键词

Keywords

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