Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
CREMA -D: Crowd -sourced emotional multimodal actors dataset,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A pre-fusion calibration module modulates multimodal features using cross-modality support and conflict cues to improve performance on five benchmarks including sentiment analysis and audio-visual tasks.
Upper-face affective features improve model calibration in noisy audiovisual sentence recognition but add only small accuracy gains compared to mouth features.
An emotion prediction model using 3-layer CNN plus AFME algorithm on speech and image data detects seven basic emotions and sarcasm at 85-96% accuracy, addressing cultural challenges in Black African conversational AI.
citing papers explorer
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Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
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Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals
A pre-fusion calibration module modulates multimodal features using cross-modality support and conflict cues to improve performance on five benchmarks including sentiment analysis and audio-visual tasks.
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Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty
Upper-face affective features improve model calibration in noisy audiovisual sentence recognition but add only small accuracy gains compared to mouth features.
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Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection
An emotion prediction model using 3-layer CNN plus AFME algorithm on speech and image data detects seven basic emotions and sarcasm at 85-96% accuracy, addressing cultural challenges in Black African conversational AI.