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Found 10 papers, 3 papers with code
Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial).
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.
Difference-Masking: Choosing What to Mask in Continued Pretraining
The self-supervised objective of masking-and-predicting has led to promising performance gains on a variety of downstream tasks.
Expanding the Role of Affective Phenomena in Multimodal Interaction Research
We find that this body of research has primarily focused on enabling machines to recognize and express affect and emotion.
Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures
In our multicultural world, affect-aware AI systems that support humans need the ability to perceive affect across variations in emotion expression patterns across cultures.
Causal Discovery
Cultural Vocal Bursts Intensity Prediction
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Speech Representations and Phoneme Classification for Preserving the Endangered Language of Ladin
To address this problem in the context of Ladin, our paper presents the first analysis of speech representations and machine learning models for classifying 32 phonemes of Ladin.
Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection
Our results motivate future work on unsupervised, affect-aware computational approaches for detecting deception and other social behaviors in the wild.
Modeling User Empathy Elicited by a Robot Storyteller
Our research informs and motivates future development of empathy perception models that can be leveraged by virtual and robotic agents during human-machine interactions.
Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection
Our subspace-alignment (SA) approach adapts audio-visual representations of deception in lab-controlled low-stakes scenarios to detect deception in real-world, high-stakes situations.
Introducing Representations of Facial Affect in Automated Multimodal Deception Detection
This approach achieved a higher AUC than existing automated machine learning approaches that used interpretable visual, vocal, and verbal features to detect deception in this dataset, but did not use facial affect.
