AesCanvas: A Large-Scale Dataset and Benchmark for Aesthetic Critique and Contextual Suitability

arXiv:2608.26713 · cs.CV, cs.AI · Submitted 2026-08-27 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-27

Updated: 2026-08-27

Terminology

• Accuracy — 46× in this paper · explained in 1 episode
• Grounding — 32× in this paper · explained in 3 episodes
• Benchmark — 26× in this paper · explained in 2 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Visual Grounding — 12× in this paper · explained in 2 episodes
• Provenance — 9× in this paper · explained in 4 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Image Aesthetic Assessment — 7× in this paper
• Cooperation — 6× in this paper · explained in 2 episodes
• Semantic Similarity — 5× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Multimodal Reasoning — 4× in this paper · explained in 5 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Affordance — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Alignment Scores — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
• Qwen3-VL Technical Report
• UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
• ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies
• AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception
• AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
• Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment
• Benchmarking Deflection and Hallucination in Large Vision-Language Models
• MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
• BERTScore: Evaluating Text Generation with BERT
• Teaching LMMs for Image Quality Scoring and Interpreting
• ReactBench: A Cause-Driven Benchmark for Multimodal Hallucination via Systematic Evaluation
• UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
• MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
• The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers

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