Research map / Detection and segmentation / Open-set and few-shot detection
Automatic High Resolution Wire Segmentation and Removal
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Ours (MiT-b2) instead of the TTPLA model | WireSegHR wire segmentation · WireSegHR | IoU (%): 3.5 -> 60.8 | Helped |
| Use Ours (ResNet-50) instead of the TTPLA model | Wire segmentation · TTPLA (Power Line only) | IoU (%): 18.9 -> 33.1 | Helped |
| Use Ours (MiT-b2) instead of Ours (ResNet-50) (ablation) | Wire segmentation · TTPLA (Power Line only) | IoU (%): 33.1 -> 42.7 | Helped |
| Use Ours (MiT-b2) instead of Ours (ResNet-50) (ablation) | Wire segmentation · WireSegHR | IoU (%): 47.8 -> 60.8 | Helped |
Closest papers by meaning
- Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer (AAAI 2025 · similarity 0.79)
- NEAT: Distilling 3D Wireframes from Neural Attraction Fields (CVPR 2024 · similarity 0.79)
- Learning to Remove Wrinkled Transparent Film with Polarized Prior (CVPR 2024 · similarity 0.79)
- High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity (ICLR 2025 · similarity 0.78)
- Label-Efficient Data Augmentation with Video Diffusion Models for Guidewire Segmentation in Cardiac Fluoroscopy (AAAI 2025 · similarity 0.78)
- EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation (CVPR 2024 · similarity 0.78)
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