RED-Aes learns aesthetic changes from edit-induced image pairs and a new RED-20k dataset via three-stage relative ranking training, claiming SOTA generalization over absolute MOS regression.
UNIAA: A unified multi-modal image aesthetic assessment baseline and benchmark
5 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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PlanBench-V is a new benchmark and dataset for evaluating VLMs on spatial planning map interpretation via a four-stage framework of Perception, Reasoning, Association, and Implementation.
PPaint fuses expert pairwise preferences and ratings into ground truth; PSDistill converts VLM pairwise judgments into calibrated pseudo-scores via Elo and trains the same VLM to produce a single-pass aesthetic scorer that improves SRCC across categories.
COMPASS is a unified multimodal framework using a shared expert token τ_c to ground composition-intent for both perception and generation, backed by the new Comp-11 dataset.
MLLMs generate verbose, comprehensive, and repetitive aesthetic critiques unlike selective human ones, and reference-based metrics fail to detect this because they capture model house style instead of image-specific content.
citing papers explorer
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Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment
RED-Aes learns aesthetic changes from edit-induced image pairs and a new RED-20k dataset via three-stage relative ranking training, claiming SOTA generalization over absolute MOS regression.
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PlanBench-V: A Spatial Planning Map Benchmark for Vision-Language Models
PlanBench-V is a new benchmark and dataset for evaluating VLMs on spatial planning map interpretation via a four-stage framework of Perception, Reasoning, Association, and Implementation.
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Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation
PPaint fuses expert pairwise preferences and ratings into ground truth; PSDistill converts VLM pairwise judgments into calibrated pseudo-scores via Elo and trains the same VLM to produce a single-pass aesthetic scorer that improves SRCC across categories.
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COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models
COMPASS is a unified multimodal framework using a shared expert token τ_c to ground composition-intent for both perception and generation, backed by the new Comp-11 dataset.
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Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models
MLLMs generate verbose, comprehensive, and repetitive aesthetic critiques unlike selective human ones, and reference-based metrics fail to detect this because they capture model house style instead of image-specific content.