  The rapid development of Multi-modality Large Language Models (MLLMs) has
navigated a paradigm shift in computer vision, moving towards versatile
foundational models. However, evaluating MLLMs in low-level visual perception
and understanding remains a yet-to-explore domain. To this end, we design
benchmark settings to emulate human language responses related to low-level
vision: the low-level visual perception (A1) via visual question answering
related to low-level attributes (e.g. clarity, lighting); and the low-level
visual description (A2), on evaluating MLLMs for low-level text descriptions.
Furthermore, given that pairwise comparison can better avoid ambiguity of
responses and has been adopted by many human experiments, we further extend the
low-level perception-related question-answering and description evaluations of
MLLMs from single images to image pairs. Specifically, for perception (A1), we
carry out the LLVisionQA+ dataset, comprising 2,990 single images and 1,999
image pairs each accompanied by an open-ended question about its low-level
features; for description (A2), we propose the LLDescribe+ dataset, evaluating
MLLMs for low-level descriptions on 499 single images and 450 pairs.
Additionally, we evaluate MLLMs on assessment (A3) ability, i.e. predicting
score, by employing a softmax-based approach to enable all MLLMs to generate
quantifiable quality ratings, tested against human opinions in 7 image quality
assessment (IQA) datasets. With 24 MLLMs under evaluation, we demonstrate that
several MLLMs have decent low-level visual competencies on single images, but
only GPT-4V exhibits higher accuracy on pairwise comparisons than single image
evaluations (like humans). We hope that our benchmark will motivate further
research into uncovering and enhancing these nascent capabilities of MLLMs.
Datasets will be available at https://github.com/Q-Future/Q-Bench.
