Summary of Dog-iqa: Standard-guided Zero-shot Mllm For Mix-grained Image Quality Assessment, by Kai Liu et al.
Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment
by Kai Liu, Ziqing Zhang, Wenbo Li, Renjing Pei, Fenglong Song, Xiaohong Liu, Linghe Kong, Yulun Zhang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Dog-IQA, a zero-shot mix-grained IQA method that utilizes the prior knowledge of multimodal large language models (MLLMs) without requiring any training data. The approach is designed to mimic human experts in assessing image quality and achieves state-of-the-art performance compared to training-free methods and competitive performance compared to training-based methods in cross-dataset scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dog-IQA helps measure how good or bad an image is by using special language models that know a lot about different things. It does this without needing any practice data, just like humans do when they look at pictures. The method uses two techniques: one that gives scores based on specific standards and another that looks at both small parts of the picture and the whole thing to get an accurate score. |
Keywords
» Artificial intelligence » Zero shot
