The workflow of ICMC (Three modal’s example).

<div><p>Multi-modal classification aims to extract pertinent information from various modalities to assign labels to instances. The advent of deep neural networks has significantly advanced this task. However, the majority of current deep neural networks lack interpretability, leading to...

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Bibliographic Details
Main Author:	Jin Jin (342682) (author)
Other Authors:	Fan Wang (135182) (author), Shengzheng Tian (22177568) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified reducing computational latency grading teaching plan extract pertinent information discerning anomalous information deep neural networks deep neural network driven attention mechanism assessing attention scores medical datasets confirm clip ), establishing modal classification tasks modal classification framework modal classification aims xlink "> multi meticulously curate multi confidence probability mechanism bolster result confidence icmc outperforms state c </ b modal datasets attention visualization icmc ), </ b enhances confidence various modalities trust deficit significantly advanced sensitive domains quantitative experiments particularly pronounced output layer openly available lassification model intermediate layer improving performance higher generalizability global perspectives based approaches assign labels art models approach incorporates 18 %.
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The workflow of ICMC (Three modal’s example).

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