Q-Instruct: Improving Low-level Visual Abilities for Multi-modality
  Foundation Models
Authors: name: Haoning Wu, name: Zicheng Zhang, name: Erli Zhang, name: Chaofeng Chen, name: Liang Liao, name: Annan Wang, name: Kaixin Xu, name: Chunyi Li, name: Jingwen Hou, name: Guangtao Zhai, name: Geng Xue, name: Wenxiu Sun, name: Qiong Yan, name: Weisi Lin

Multi-modality foundation models, as represented by GPT-4V, have brought a
new paradigm for low-level visual perception and understanding tasks, that can
respond to a broad range of natural human instructions in a model. While
existing foundation models have shown exciting potentials on low-level visual
tasks, their related abilities are still preliminary and need to be improved.
In order to enhance these models, we conduct a large-scale subjective
experiment collecting a vast number of real human feedbacks on low-level
vision. Each feedback follows a pathway that starts with a detailed description
on the low-level visual appearance (*e.g. clarity, color, brightness* of an
image, and ends with an overall conclusion, with an average length of 45 words.
The constructed **Q-Pathway** dataset includes 58K detailed human feedbacks on
18,973 images with diverse low-level appearance. Moreover, to enable foundation
models to robustly respond to diverse types of questions, we design a
GPT-participated conversion to process these feedbacks into diverse-format 200K
instruction-response pairs. Experimental results indicate that the
**Q-Instruct** consistently elevates low-level perception and understanding
abilities across several foundational models. We anticipate that our datasets
can pave the way for a future that general intelligence can perceive,
understand low-level visual appearance and evaluate visual quality like a
human. Our dataset, model zoo, and demo is published at:
https://q-future.github.io/Q-Instruct.