activity
20142024
most citedObject Detectors Emerge in Deep Scene CNNs

715 citations · 891 across the 6 of their papers we have counts for

collaborators

6 papers

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…

cs.CV2022

Local Relighting of Real Scenes

Audrey Cui, Ali Jahanian, Agata Lapedriza +4

We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image. This new task differs…

cs.CV2016175 cited

Places: An Image Database for Deep Scene Understanding

Bolei Zhou, Aditya Khosla, Agata Lapedriza +2

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene…

cs.CV2014715 cited

Object Detectors Emerge in Deep Scene CNNs

Bolei Zhou, Aditya Khosla, Agata Lapedriza +2

With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled exampl…