Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework | ScienceToStartup | ScienceToStartup

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References (42)

[1]

Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking

2025Wen Wen, Tianwu Zhi et al.

[2]

Deep Opinion-Unaware Blind Image Quality Assessment by Learning and Adapting from Multiple Annotators

2025Zhihua Wang, Xuelin Liu et al.

[3]

LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs

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"Develop a label-efficient image quality assessment tool using a distillation framework to minimize annotation costs."

Computer Vision•Score: 6•View PDF ↗

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Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework

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References (42)

Founder's Pitch

"Develop a label-efficient image quality assessment tool using a distillation framework to minimize annotation costs."

Commercial Viability Breakdown

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Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework

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References (42)

Founder's Pitch

"Develop a label-efficient image quality assessment tool using a distillation framework to minimize annotation costs."

Commercial Viability Breakdown

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