In todays competitive landscape, businesses grapple with customer retention.
Churn prediction models, although beneficial, often lack accuracy due to the
reliance on a single data source. The intricate nature of human behavior and
high dimensional customer data further complicate these efforts. To address
these concerns, this paper proposes a multimodal fusion learning model for
identifying customer churn risk levels in financial service providers. Our
multimodal approach integrates customer sentiments financial literacy (FL)
level, and financial behavioral data, enabling more accurate and bias-free
churn prediction models. The proposed FL model utilizes a SMOGN COREG
supervised model to gauge customer FL levels from their financial data. The
baseline churn model applies an ensemble artificial neural network and
oversampling techniques to predict churn propensity in high-dimensional
financial data. We also incorporate a speech emotion recognition model
employing a pre-trained CNN-VGG16 to recognize customer emotions based on
pitch, energy, and tone. To integrate these diverse features while retaining
unique insights, we introduced late and hybrid fusion techniques that
complementary boost coordinated multimodal co learning. Robust metrics were
utilized to evaluate the proposed multimodal fusion model and hence the
approach validity, including mean average precision and macro-averaged F1
score. Our novel approach demonstrates a marked improvement in churn
prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP)
score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid
fusion learning technique compared with late fusion and baseline models.
Furthermore, the analysis demonstrates a positive correlation between negative
emotions, low FL scores, and high-risk customers.

本研究提出了一种多模态融合学习模型，用于识别金融服务提供商中客户流失风险水平，并通过引入迟误融合和混合融合技术取得了显著的流失预测改进，实现了 91.2％的测试准确率、66 的平均准确性和 54 的宏平均 F1 得分，同时还发现了负面情绪、低金融素养得分和高风险客户之间的正相关关系。

多模融合学习的客户流失预测：融合客户金融素养、声音和行为数据

Churn Prediction via Multimodal Fusion Learning:Integrating Customer  Financial Literacy, Voice, and Behavioral Data

In recent years, many convolutional neural network-based models are designed
for JPEG artifacts reduction, and have achieved notable progress. However, few
methods are suitable for extreme low-bitrate image compression artifacts
reduction. The main challenge is that the highly compressed image loses too
much information, resulting in reconstructing high-quality image difficultly.
To address this issue, we propose a multimodal fusion learning method for
text-guided JPEG artifacts reduction, in which the corresponding text
description not only provides the potential prior information of the highly
compressed image, but also serves as supplementary information to assist in
image deblocking. We fuse image features and text semantic features from the
global and local perspectives respectively, and design a contrastive loss built
upon contrastive learning to produce visually pleasing results. Extensive
experiments, including a user study, prove that our method can obtain better
deblocking results compared to the state-of-the-art methods.

本文提出一种基于多模态融合学习的文本引导下的 JPEG 压缩伪影降低方法，通过从全局和局部角度融合图像和文本语义特征，并基于对比学习构建对比损失函数，能够在极低码率下有效降低 JPEG 压缩伪影。经过实验证明，该方法能够获得比现有技术更好的降低伪影效果。