The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Open accessAbstract
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically. Specifically, we first describe all candidate label combinations (from multiple tasks) using a textual template, and compute the joint probability from the cosine similarities of the visual-textual embeddings. Predictions of each task can be inferred from the joint distribution, and optimized by carefully designed loss functions. Through comprehensive experiments on learning three tasks - BIQA, scene classification, and distortion type identification, we verify that the proposed BIQA method 1) benefits from the scene classification and distortion type identification tasks and outperforms the state-of-the-art on multiple IQA datasets, 2) is more robust in the group maximum differentiation competition, and 3) realigns the quality annotations from different IQA datasets more effectively. The source code is available at https://github.com/zwx8981/LIQE.
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Cites
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“[79] suggested prompt tuning to improve transfer effectiveness, at the cost of language interpretability.”
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Cited by
Quality assessment in the era of large models: A survey
Zhang, Z., Zhou, Y., Li, C., Zhao, B., Liu, X., & Zhai, G. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3722559 · Cited by 23
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Metadata
- Authors
- Zhang, W., Zhai, G., Wei, Y., Yang, X., & Ma, K.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 14071-14081
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076169
- Cited by
- 325
- References
- 75
- FWCI
- 36.95
- Type
- conference paper
- Language
- en
- Open access
- green
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