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Related Experiment Video

Updated: Jun 24, 2026

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Bridging Subjectivity in Affective Explanation Captioning via Consensus-Prompted Emotion Reasoning.

Peipei Song, Zhiyan Zhang, Weidong Chen

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 22, 2026

Summary

This summary is machine-generated.

This study introduces Consensus-Prompted Emotion Reasoning (CPER) to improve Affective Explanation Captioning (AEC). CPER effectively handles emotional diversity and aligns visual content with emotions for more accurate image-based emotion analysis.

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Area of Science:

Computer Vision
Artificial Intelligence
Cognitive Science

Background:

Affective Explanation Captioning (AEC) aims to analyze viewer-centered emotions in images and explain their causes.
Existing methods struggle with emotional subjectivity and the gap between abstract emotions and visual content.

Purpose of the Study:

To propose a novel framework, Consensus-Prompted Emotion Reasoning (CPER), to address AEC challenges.
To model emotional diversity and enforce emotional-semantic alignment for improved visual emotion analysis.

Main Methods:

Developed a consensus-based bucket prompt to manage emotional diversity.
Introduced a dual-space representation using CLIP and emotion encoders for semantic and affective cues.
Implemented an emotion consistency learning strategy for grounded explanations.

Main Results:

CPER demonstrated effectiveness in handling emotional diversity and semantic coherence.
Experiments on ArtEmis v1.0, ArtEmis v2.0, and Affection datasets showed superior performance.
The framework achieved better results compared to state-of-the-art methods in AEC.

Conclusions:

CPER successfully bridges the gap between subjective emotions and objective visual content.
The proposed methods enhance the accuracy and coherence of affective explanation captions.
CPER offers a promising approach for viewer-centered visual emotion analysis.