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Current image quality metrics struggle to articulate *why* one high-quality image is better than another, but this challenge shows MLLMs are closing the gap by providing expert-level explanations.
Current image restoration models still fail to strike the right balance between noise reduction, detail fidelity, and accurate color in real-world, low-light portrait scenarios, highlighting a critical gap this challenge aims to close.
Stop training your image restoration models to mimic flawed ground truth; instead, explicitly optimize for perceptual quality using a plug-and-play module guided by No-Reference Image Quality Assessment.
Instead of discarding noisy pseudo-labels in image restoration, QualiTeacher leverages them by teaching the model to understand and even surpass the quality levels they represent.
Current vision-language models are surprisingly bad at surgical safety reasoning, failing to integrate phase information to identify safe operative zones, but a new RLHF-tuned model closes the gap.
