ConvGenVisMo: Evaluation of Conversational Generative Vision Models
Narjes Nikzad Khasmakhi Meysam Asgari-Chenaghlu Nabiha Asghar Philipp Schaer Dietlind Zühlke
Conversational generative vision models (CGVMs) like Visual ChatGPT (Wu et
al., 2023) have recently emerged from the synthesis of computer vision and
natural language processing techniques. These models enable more natural and
interactive communication between humans and machines, because they can
understand verbal inputs from users and generate responses in natural language
along with visual outputs. To make informed decisions about the usage and
deployment of these models, it is important to analyze their performance
through a suitable evaluation framework on realistic datasets. In this paper,
we present ConvGenVisMo, a framework for the novel task of evaluating CGVMs.
ConvGenVisMo introduces a new benchmark evaluation dataset for this task, and
also provides a suite of existing and new automated evaluation metrics to
evaluate the outputs. All ConvGenVisMo assets, including the dataset and the
evaluation code, will be made available publicly on GitHub.