Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add memory networks to the colorization network (ablation) | MemoPainter · Oxford102 Flower, Monster, Yumi, Superheroes, and Pokemon datasets | Colorization quality | Helped |
| Add memory networks to the colorization network (ablation) | MemoPainter · Pokemon one-shot colorization | Color identity preservation | Helped |
| Use unsupervised threshold triplet loss instead of supervised class labels | MemoPainter memory networks · 5-way and 15-way few-shot classification | Classification accuracy: 87.50% vs 91.66%; 87.50% vs 87.50%; 69.44% vs 72.22%; 70.83% vs 75.00% | Mixed |
| Represent stored colors with ten dominant RGB values rather than color distributions | MemoPainter · One-shot colorization | Colorization performance | Helped |
| Use threshold triplet loss to train memory retrieval without class labels | MemoPainter memory networks · Datasets with consistent class colors | Relevance of retrieved color features | Helped |
| Apply threshold triplet loss to data with arbitrary class colors | MemoPainter memory networks · Datasets where colors are arbitrary | Colorization performance | Hurt |
Closest papers by meaning
- Colorization Transformer (ICLR 2021 · similarity 0.87)
- Instance-Aware Image Colorization (CVPR 2020 · similarity 0.86)
- Learning Diverse Image Colorization (CVPR 2017 · similarity 0.86)
- Automatic Controllable Colorization via Imagination (CVPR 2024 · similarity 0.85)
- MangaNinja: Line Art Colorization with Precise Reference Following (CVPR 2025 · similarity 0.84)
- Efficient Neural Network Encoding for 3D Color Lookup Tables (AAAI 2025 · similarity 0.83)
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