Research map / Image generation / Image restoration
Composition-Preserving Deep Photo Aesthetics Assessment
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use native-size, untransformed images with adaptive spatial pooling instead of cropping, scaling, or padding | VGG-based aesthetics assessment · AVA | accuracy: 71.2% -> 77.1%; 73.8% -> 77.1%; 72.9% -> 77.1% | Helped |
| Use multi-net adaptive pooling rather than spatial pyramid pooling | VGG-based aesthetics assessment · AVA | accuracy: 76.0% -> 77.1% | Helped |
| Add scene-aware aggregation of sub-network predictions (ablation) | MNA-CNN · AVA (δ = 1 training labels) | accuracy: 76.1% -> 76.5% | Helped |
| Add scene-aware aggregation of sub-network predictions (ablation) | MNA-CNN · AVA (δ = 0 training labels) | accuracy: 77.1% -> 77.4% | Helped |
| Use native-size composition-preserving processing rather than transformed-image baselines | MNA-CNN composition-change assessment · 40 original/transformed image pairs | agreement with user-rated pair ordering: all these 40 pairs; 59.5%; 61.5%; 63.1% | Helped |
Closest papers by meaning
- A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment (CVPR 2017 · similarity 0.92)
- Advancing Comprehensive Aesthetic Insight with Multi-Scale Text-Guided Self-Supervised Learning (AAAI 2025 · similarity 0.85)
- ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression (NeurIPS 2025 · similarity 0.85)
- Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment (AAAI 2026 · similarity 0.85)
- Charm: The Missing Piece in ViT Fine-Tuning for Image Aesthetic Assessment (CVPR 2025 · similarity 0.84)
- Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping (CVPR 2026 · similarity 0.84)
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