Research map / Label-efficient and robust learning / Recognition and long-tail
SketchNet: Sketch Classification With Web Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use web-image references and sketch–positive–negative triplets instead of sketch-only training | SketchNet · TU-Berlin (72 train/category) | MAP: 75.02% -> 80.42% | Helped |
| Use web-image references and sketch–positive–negative triplets instead of mixing sketch and real images in AlexNet training | SketchNet · TU-Berlin (72 train/category) | MAP: 73.25% -> 80.42% | Helped |
| Add metric learning to merge test-pair scores (ablation) | SketchNet · TU-Berlin (72 train/category) | MAP: 77.41% -> 80.42% | Helped |
| Add metric learning to merge test-pair scores (ablation) | SketchNet · TU-Berlin (16 train/category) | MAP: 64.37% -> 64.43% | No clear effect |
Closest papers by meaning
- Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse (AAAI 2019 · similarity 0.91)
- SketchGAN: Joint Sketch Completion and Recognition With Generative Adversarial Network (CVPR 2019 · similarity 0.91)
- Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch (CVPR 2025 · similarity 0.89)
- SketchINR: A First Look into Sketches as Implicit Neural Representations (CVPR 2024 · similarity 0.88)
- SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models (CVPR 2025 · similarity 0.87)
- ARNet: Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling (AAAI 2025 · similarity 0.87)
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