AICA-Bench evaluates 23 VLMs on affective image analysis, identifies weak intensity calibration and shallow descriptions as limitations, and proposes training-free Grounded Affective Tree Prompting to improve performance.
Ran Xu, Xinyi Wang, Jie Chen, et al
2 Pith papers cite this work. Polarity classification is still indexing.
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Soul Computing is introduced as a framework distinguishing narrow and broad forms for constructing intelligent agents with self-identity via intensional cores, separate from affective computing or virtual humans.
citing papers explorer
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AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis
AICA-Bench evaluates 23 VLMs on affective image analysis, identifies weak intensity calibration and shallow descriptions as limitations, and proposes training-free Grounded Affective Tree Prompting to improve performance.
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Soul Computing: A Theoretical Framework and Technical Architecture for Intelligent Agents with Independent Consciousness
Soul Computing is introduced as a framework distinguishing narrow and broad forms for constructing intelligent agents with self-identity via intensional cores, separate from affective computing or virtual humans.