activity
20232026
most citedAnalyzing the contribution of different passively collected data to predict Stress and Depression

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms

Sree Bhattacharyya, Manas Mehta, Leona Chen +4

The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and varies systematically acros…

cs.CV2025

Automated Detection of Visual Attribute Reliance with a Self-Reflective Agent

Christy Li, Josep Lopez Camuñas, Jake Thomas Touchet +4

When a vision model performs image recognition, which visual attributes drive its predictions? Detecting unintended reliance on specific visual features is critical for ensuring mo…

cs.CV2025

Experimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults

Josep Lopez Camunas, Cristina Bustos, Yanjun Zhu +2

Understanding emotional signals in older adults is crucial for designing virtual assistants that support their well-being. However, existing affective computing models often face s…

cs.CL2024

Analyzing Cultural Representations of Emotions in LLMs through Mixed Emotion Survey

Shiran Dudy, Ibrahim Said Ahmad, Ryoko Kitajima +1

Large Language Models (LLMs) have gained widespread global adoption, showcasing advanced linguistic capabilities across multiple of languages. There is a growing interest in academ…

cs.LG20231 cited

Analyzing the contribution of different passively collected data to predict Stress and Depression

Irene Bonafonte, Cristina Bustos, Abraham Larrazolo +6

The possibility of recognizing diverse aspects of human behavior and environmental context from passively captured data motivates its use for mental health assessment. In this pape…

cs.CV2023

On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP

Cristina Bustos, Carles Civit, Brian Du +2

This work presents a study on how to exploit the CLIP embedding space to perform Visual Sentiment Analysis. We experiment with two architectures built on top of the CLIP embedding…