VIEScore: Towards Explainable Metrics for Conditional Image Synthesis
  Evaluation
Max Ku Dongfu Jiang Cong Wei Xiang Yue Wenhu Chen
In the rapidly advancing field of conditional image generation research,
challenges such as limited explainability lie in effectively evaluating the
performance and capabilities of various models. This paper introduces VIESCORE,
a Visual Instruction-guided Explainable metric for evaluating any conditional
image generation tasks. VIESCORE leverages general knowledge from Multimodal
Large Language Models (MLLMs) as the backbone and does not require training or
fine-tuning. We evaluate VIESCORE on seven prominent tasks in conditional image
tasks and found: (1) VIESCORE (GPT4-v) achieves a high Spearman correlation of
0.3 with human evaluations, while the human-to-human correlation is 0.45. (2)
VIESCORE (with open-source MLLM) is significantly weaker than GPT-4v in
evaluating synthetic images. (3) VIESCORE achieves a correlation on par with
human ratings in the generation tasks but struggles in editing tasks. With
these results, we believe VIESCORE shows its great potential to replace human
judges in evaluating image synthesis tasks.