detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2403.14783
Tema
Computer Science - Computer Vision... Computer Science - Artificial Inte... Computer Science - Computation and... Computer Science - Machine Learnin... Computer Science - Multiagent Syst...Autor
Jiang, Bowen Zhuang, Zhijun Shivakumar, Shreyas S. Roth, Dan Taylor, Camillo J.Categoría
Computer Science
Año
2024
fecha de cotización
27/3/2024
Palabras clave
foundation models vqa multi-agent science computerResumen
This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks.
We propose an adaptive multi-agent system, named Multi-Agent VQA, to overcome the limitations of foundation models in object detection and counting by using specialized agents as tools.
Unlike existing approaches, our study focuses on the system's performance without fine-tuning it on specific VQA datasets, making it more practical and robust in the open world.
We present preliminary experimental results under zero-shot scenarios and highlight some failure cases, offering new directions for future research.
;Comment: A full version of the paper will be released soon.
The codes are available at https://github.com/bowen-upenn/Multi-Agent-VQA
Jiang, Bowen,Zhuang, Zhijun,Shivakumar, Shreyas S.,Roth, Dan,Taylor, Camillo J., 2024, Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish
evaluate provide turkishmmlu questions language
The Measurement of Acculturation in Neuropsychological Evaluations of Hispanic/Latino Individuals across the Lifespan: A Scoping Review of the Literature
evidence review lifespan various validation acculturation measures
