OASIS: An Open-dimension Assessment System for Image Scoring via Definition-Driven Visual Evidence Learning

Image filtering and vision-system monitoring require application-specific criteria that may change after an assessment model has been trained. However, models trained for predefined assessment objectives offer limited flexibility when new criteria are introduced. To address this limitation, we study open-dimension image scoring, in which a model predicts a continuous score from an image and a natural-language evaluation definition. The focus is on criteria not used as scoring targets during task-specific training, with model parameters fixed at inference. To this end, we propose OASIS (Open-dimension Assessment System for Image Scoring), a two-stage framework that connects evaluation definitions, criterion-relevant visual evidence and continuous scores. Specifically, definition-conditioned descriptive supervision is first used to guide the model toward visual evidence relevant to the specified criterion. The model is then trained for scoring with semantics-preserving definition augmentation and ordinal distribution learning, which encourage reliance on definition content and account for the ordering and distance between score levels. To support training and evaluation, we construct MD61, comprising 40 base dimensions with scores and evidence descriptions curated through a Score-and-Rank Verification Process, and 21 held-out dimensions annotated by five computer vision experts. Experimental results on the held-out dimensions show that OASIS outperforms evaluated baselines, such as conventional and language-driven image quality assessment (IQA) methods, and achieves an overall PLCC/SRCC of 0.829/0.823. Further experiments demonstrate that its scores correlate with downstream vision performance under controlled image degradations and that multidimensional supervision improves cross-dataset transfer on conventional IQA benchmarks. These results support the feasibility of adapting image assessment to newly specified criteria through natural-language definitions without retraining.