Research map / Label-efficient and robust learning / Recognition and long-tail
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 2a) | ME (%): 6.00 -> 0.67 | Helped |
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 2b) | ME (%): 16.22 -> 2.00 | Helped |
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 2c) | ME (%): 14.44 -> 2.33 | Helped |
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 3d) | ME (%): 8.05 -> 5.23 | Helped |
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 3e) | ME (%): 8.33 -> 7.12 | Helped |
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 3f) | ME (%): 17.38 -> 5.38 | Helped |
| Use MCT instead of PEARL | Line and conic fitting · Line and conic synthetic (Fig. 3g) | ME (%): 19.21 -> 6.23 | Helped |
| Use MCT instead of T-linkage | Multiple homography fitting · AdelaideRMF homography dataset (mean) | ME (%): 6.60 -> 6.13 | Helped |
| Use MCT instead of Multi-X | Multiple homography fitting · AdelaideRMF homography dataset (median) | ME (%): 2.49 -> 4.93 | Hurt |
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