Q-ALIGN: Revolutionizing Visual Content Assessment with Human-Like Judgment

Advances in Machine-Based Visual Assessment

In the ever-expanding digital world, accurately assessing images and videos is crucial. Q-ALIGN, a new methodology developed by researchers at Nanyang Technological University, Shanghai Jiao Tong University, and SenseTime Research, offers a significant breakthrough.

Training with a focus on text-defined rating levels, not direct numerical scores, Q-ALIGN reflects how humans judge visual content. This approach results in more accurate assessments and better generalization to new content types.

During inference, Q-ALIGN mirrors the process of collecting mean opinion scores (MOS) from human ratings. It has shown superior performance in image and video quality assessment (IQA), image aesthetic assessment (IAA), and video quality assessment (VQA) tasks.

This new methodology addresses the limitations of traditional approaches and offers a more robust, intuitive tool for scoring diverse types of visual content. Its potential for broad application across various fields marks a paradigm shift in the domain of visual content assessment.

For more information and to access the research paper and Github, visit https://arxiv.org/abs/2312.17090. And don’t forget to follow us on Twitter for more updates on our work.

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