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.
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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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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Revolutionizing Image Quality Assessment: The Introduction of Co-Instruct and MICBench for Enhanced Visual Comparisons
- MarkTechPost
- Twitter – @itinaicom
