arXiv 2509.25787 introduces EvoQuality, a ByteDance method for training VLMs to assess image quality with zero human annotation. The standout: the method uses "self-voting + GRPO" to bootstrap a quality assessment model from an unlabeled image dataset, achieving SOTA on standard image quality benchmarks.
The "zero-annotation quality assessment" problem: training a VLM to assess image quality typically requires a large dataset of images labeled with quality scores. This is expensive to collect — humans must rate each image, and the ratings are subjective. EvoQuality's fix: bootstrap a quality model without any human labels.
The "self-voting + GRPO" mechanism: the model is trained to predict quality scores that are consistent with its own predictions across multiple augmentations (e.g., a slightly blurred image should have a slightly lower quality score than the original). The "self-voting" provides the training signal — the model's own predictions on augmented images serve as "pseudo-labels." The "GRPO" (Group Relative Policy Optimization) ensures the model learns to rank images, not just predict absolute scores.
The benchmark: on the KonIQ-10k and SPAQ benchmarks, EvoQuality-trained VLMs hit SOTA, beating models trained on human-labeled data. The "zero-annotation" claim is verified — the training uses no human quality labels.
The bigger takeaway: "self-supervised quality assessment" is a significant new direction. The "human labels are necessary" assumption is breaking, and the "self-voting" approach is a clean solution. For the industry, this means "image quality assessment" can be added to VLMs without expensive human annotation, and the next generation of "quality-aware" image generation models will use self-supervised techniques.