A novel methodology called Q-ALIGN, developed by researchers from Nanyang Technological University, Shanghai Jiao Tong University, and SenseTime Research, marks a paradigm shift in visual content assessment. It uses text-defined rating levels to train Large Multi-Modality Models, achieving state-of-the-art performance in assessing image and video quality, aesthetic, and alignments with human judgment.
Introducing Q-Align: The All-in-One Visual Scorer Based on Large Multi-Modality Models
As middle managers dealing with a vast amount of visual content, it’s crucial to assess images and videos accurately. Traditional methods have limitations, especially with the complexity and variety of modern visual content. This is where Q-ALIGN, a novel methodology, comes in.
The Breakthrough: Q-ALIGN
Q-ALIGN represents a departure from conventional approaches and educates Large Multi-Modality Models (LMMs) to rate visual content using text-defined rating levels, not direct numerical scores. This approach is more akin to how human raters evaluate and judge in subjective studies, marking a significant shift in machine-based visual assessment.
How Q-ALIGN Works
During the training phase, Q-ALIGN converts existing score labels into discrete text-defined rating levels, teaching LMMs to understand and use these text-defined levels for visual rating, which aligns more with human cognitive processes. In the inference phase, it emulates the strategy of collecting mean opinion scores (MOS) from human ratings, achieving state-of-the-art performance in IQA, IAA, and VQA tasks.
Practical Applications and Value
Q-ALIGN’s ability to generalize effectively to new types of content underlines its potential for broad application across various fields. It represents a paradigm shift in the domain of visual content assessment, offering a robust, accurate, and more intuitive tool for scoring diverse types of visual content.
If you want to evolve your company with AI, stay competitive, and benefit from practical AI solutions like Q-ALIGN, it’s essential to identify automation opportunities, define KPIs, select an AI solution, and implement gradually.
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