Revolutionizing Image Quality Assessment: The Introduction of Co-Instruct and MICBench for Enhanced Visual Comparisons

The method of Image Quality Assessment (IQA) standardizes image evaluation by incorporating subjective studies and large multimodal models (LMMs). LMMs capture nuanced understanding of data, improving performance across tasks. Researchers from multiple universities proposed Co-Instruct, a dataset for open-ended multi-image quality comparison, resulting in significant improvements over existing LMMs. This revolutionizes image quality assessment.

 Revolutionizing Image Quality Assessment: The Introduction of Co-Instruct and MICBench for Enhanced Visual Comparisons

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Revolutionizing Image Quality Assessment: The Introduction of Co-Instruct and MICBench for Enhanced Visual Comparisons

Image Quality Assessment (IQA) and Co-Instruct

Image Quality Assessment (IQA) measures various aspects of images to improve visual content evaluation. Co-Instruct, a new model, enhances IQA by enabling open-ended evaluations and detailed reasoning beyond overall scores.

Benefits of Co-Instruct

Co-Instruct, developed by researchers from Nanyang Technological University, City University of Hong Kong, Shanghai Jiao Tong University, and SenseTime Research, achieves up to 86% improvement over baseline models and outperforms open-source LMMs. It also demonstrates comparable proficiency to GPT-4V in tasks requiring detailed language reasoning.

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