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Multi-Level Context Pyramid Network for Visual Sentiment Analysis.

Haochun Ou1, Chunmei Qing1, Xiangmin Xu1

  • 1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China.

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|April 3, 2021
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Summary
This summary is machine-generated.

This study introduces a new Multi-level Context Pyramid Network (MCPNet) for visual sentiment analysis. MCPNet effectively analyzes images at multiple scales, significantly improving the accuracy of emotion detection in visual content.

Keywords:
MACMMCPNetcontextemotionsentiment analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Affective Computing

Background:

  • Social media relies heavily on visual content for emotional expression.
  • Analyzing visual sentiment is crucial due to the impact of images and videos on emotions.
  • Existing methods often overlook scale variations and emotional intensity in complex scenes.

Purpose of the Study:

  • To develop an advanced model for visual sentiment analysis.
  • To address limitations in current methods regarding scale and emotional intensity perception.
  • To improve the classification performance of visual sentiment analysis.

Main Methods:

  • Proposed a Multi-level Context Pyramid Network (MCPNet) integrating local and global representations.
  • Utilized Resnet101 as a backbone for multi-level emotional representation extraction.
  • Introduced Multi-scale Adaptive Context Modules (MACM) for region-wise sentiment correlation and feature extraction.

Main Results:

  • MCPNet demonstrated superior performance over state-of-the-art methods across seven datasets.
  • Achieved over 90% accuracy on the FI dataset.
  • Effectively combined multi-level context features for robust sentiment classification.

Conclusions:

  • The proposed MCPNet effectively captures multi-scale and multi-level emotional information.
  • The integration of local and global features enhances visual sentiment analysis.
  • MCPNet offers a significant advancement in accurately classifying emotions from visual data.