22 citations · 63 across the 11 of their papers we have counts for
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cs.CV2023★ 22 cited
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Xiaoshi Wu, Yiming Hao, Keqiang Sun +4
Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing eval…
cs.CV2023★ 7 cited
X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
Yixiong Chen, Li Liu, Chris Ding
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation m…
eess.IV2023★ 3 cited
Label-Free Liver Tumor Segmentation
Qixin Hu, Yixiong Chen, Junfei Xiao +4
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intrig…