Research map / Image generation / Super-resolution
Gradient-Based Graph Attention for Scene Text Image Super-resolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TSAN instead of TATT | STISR · TextZoom all | MORAN accuracy: 59.5 -> 62.7 | Helped |
| Use TSAN instead of TATT | STISR · TextZoom all | ASTER accuracy: 63.6 -> 64.1 | Helped |
| Use TSAN instead of TATT | STISR · TextZoom all | CRNN accuracy: 52.6 -> 53.0 | Helped |
| Add GWAM (ablation) | TSAN · TextZoom test | ASTER accuracy: 0.6% performance drop when removed | Helped |
| Add CCAM (ablation) | TSAN · TextZoom test | ASTER accuracy: 0.4% | Helped |
| Add GGAM (ablation) | TSAN · TextZoom test | ASTER accuracy: 1.6% | Helped |
| Use a gradient map in GGAM (ablation) | TSAN · TextZoom test | ASTER accuracy | Helped |
| Use M=8 groups in GWAM rather than M=1 (ablation) | TSAN · TextZoom all | ASTER accuracy: 62.6 -> 64.1 | Helped |
| Use 7 × 7 GGAM patches rather than 1 × 1 (ablation) | TSAN · TextZoom all | ASTER accuracy: 61.2 -> 64.1 | Helped |
Closest papers by meaning
- Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders (NeurIPS 2025 · similarity 0.89)
- OTE: Exploring Accurate Scene Text Recognition Using One Token (CVPR 2024 · similarity 0.86)
- SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-Conditioning (NeurIPS 2025 · similarity 0.85)
- MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling (NeurIPS 2025 · similarity 0.85)
- Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing (NeurIPS 2024 · similarity 0.85)
- Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation (CVPR 2025 · similarity 0.85)
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