Research map / Detection and segmentation / Open-set and few-shot detection
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with human-labeled BTS alongside TextSeg rather than TextSeg alone | PGTSNet(base) · BTS test | fgIoU: 64.13 -> 83.68 | Helped |
| Use TextSeg and BTS instead of TextSeg and synthetic bilingual data | PGTSNet(base) · BTS test | fgIoU: 74.59 -> 83.68 | Helped |
| Add the text perceptual discriminator (ablation) | PGTSNet(base) · BTS validation | fgIoU: 83.68 -> 84.78 | Helped |
| Add the TV loss to the discriminator variant (ablation) | PGTSNet · BTS validation | fgIoU: 84.78 -> 84.93 | Helped |
| Add the detection module to the discriminator and TV-loss variant (ablation) | PGTSNet · BTS validation | fgIoU: 84.93 -> 86.48 | Helped |
| Use PGTSNet(final) instead of the strongest listed baseline for fgIoU | BTS text segmentation · BTS test | fgIoU: 82.76 -> 86.48 | Helped |
| Use PGTSNet(final) instead of the strongest listed baseline for F-score | BTS text segmentation · BTS test | F-score: 89.24 -> 90.98 | Helped |
Closest papers by meaning
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