Nov 27, 2024 · Despite this, a comprehensive review of learning-based, particularly deep learning-based, data fusion strategies is lacking. This paper presents a thorough review of deep learning-based data fusion methodologies across various …

Despite this, a comprehensive review of learning-based, particularly deep learning-based, data fusion strategies is lacking. This paper presents a thorough review of deep learning-based data fusion methodologies across various fields, examining their evolution over the past five years.

Jan 29, 2020 · Research [6] proposes two possible approaches for early fusion technique. The first approach is combining data by removing the correlation between two sensors. The second approach is to fuse...

Feb 20, 2024 · Personally, I learn better when I intake information from multiple sources about a particular subject. For example, if I wanted to learn about the Transformer architecture, here are some of the...

Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.

May 1, 2020 · Deep learning, a hierarchical computation model, learns the multilevel abstract representation of the data (LeCun, Bengio, & Hinton, 2015). It uses the the backpropagation algorithm to train its parameters, which can transfer raw inputs to effective task-specific representations.

Aug 1, 2022 · Seven prevalent sub-fields in multimodal remote sensing data fusion are detailed. Some available resources, including tutorials, datasets, and codes, are provided. Deep learning yields great achievements in multimodal remote sensing data fusion.

Aug 27, 2023 · Data fusion is the process combining data from multiple modalities with the goal of getting complimentary information from each modality to get more accurate representations, which are better...

Deep model fusion is a technique that merges, ensemble, or fuse multiple deep neural networks to obtain a unified model. It can be used to improve the performance and robustness of model or to combine the strengths of different models, such as fuse multiple task-specific models to create a multi-task model.

Jan 1, 2020 · To clarify the methodologies of urban big data fusion based on deep learning (DL), this paper classifies them into three categories: DL-output-based fusion, DL-input-based fusion and DL-double-stage-based fusion. These methods use deep learning to learn feature representation from multi-source big data.