Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping

arXiv:2610.00582 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/zzqingz/CPIC

Terminology

• Benchmark — 25× in this paper · explained in 2 episodes
• GRPO — 16× in this paper · explained in 6 episodes
• Aesthetic Image Cropping — 10× in this paper · explained in 1 episode
• Clustering — 10× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Generalization — 8× in this paper · explained in 13 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Continuous Preference Field — 7× in this paper
• Ablation Studies — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Generative AI — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment
• Accelerating Rectified Flow Models via Trajectory-Aware Caching
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• COMEX: A Composition-Grounded Benchmark and Learning Framework for Explainable Aesthetic Image Cropping

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