MultiEmo-Bench supplies 10,344 images with aggregated multi-label emotion votes from 20 annotators each to evaluate MLLMs on dominant emotion and full distribution prediction.
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DS-IEQA jointly learns evaluation criteria via feedback-driven prompt optimization and continuous score modeling via token-decoupled distance regression, ranking 4th in the 2026 NTIRE X-AIGC Quality Assessment Track 2 without extra training data.
LAION-Aesthetics Predictor reinforces Western and male biases by preferentially selecting images associated with women and realistic Western/Japanese art while excluding men, LGBTQ+ references, and other styles.
Vision-language models encode diverse aesthetic attributes that propagate through decoder layers and enable effective personalized image aesthetics assessment using lightweight linear models without fine-tuning.
Human visual artists outperform non-artists and AI image generators on creativity ratings, with more human prompting improving AI output but not closing the gap, while human and AI raters disagree on what counts as creative.
citing papers explorer
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MultiEmo-Bench: Multi-label Visual Emotion Analysis for Multi-modal Large Language Models
MultiEmo-Bench supplies 10,344 images with aggregated multi-label emotion votes from 20 annotators each to evaluate MLLMs on dominant emotion and full distribution prediction.
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Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment
DS-IEQA jointly learns evaluation criteria via feedback-driven prompt optimization and continuous score modeling via token-decoupled distance regression, ranking 4th in the 2026 NTIRE X-AIGC Quality Assessment Track 2 without extra training data.
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The Algorithmic Gaze of Image Quality Assessment: An Audit and Trace Ethnography of the LAION-Aesthetics Predictor
LAION-Aesthetics Predictor reinforces Western and male biases by preferentially selecting images associated with women and realistic Western/Japanese art while excluding men, LGBTQ+ references, and other styles.
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What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?
Vision-language models encode diverse aesthetic attributes that propagate through decoder layers and enable effective personalized image aesthetics assessment using lightweight linear models without fine-tuning.
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Stable diffusion models reveal a persisting human and AI gap in visual creativity
Human visual artists outperform non-artists and AI image generators on creativity ratings, with more human prompting improving AI output but not closing the gap, while human and AI raters disagree on what counts as creative.