Semanic Scene Understanding

2D Instance Segmentation


Our evaluation table ranks all methods according to the Average Precision (AP) over 10 IoU thresholds, ranging from 0.5 to 0.95 with a step size of 0.05. The IoU is weighted by the confidence as \(\text{IoU} = \frac{\sum_{i\in{\{\text{TP}\}}}c_{i}}{\sum_{i\in{\{\text{TP, FP, FN}\}}}c_{i}}\) where \(\{\text{TP}\}\) and \(\{\text{TP, FP, FN}\}\) are the set of image pixels in the intersection and the union of one instance, respectively. \(c_i \in [0, 1]\) denotes the confidence value at pixel \(i\). In constrast to standard evaluation where \(c_i=1\) for all pixels, we adopt confidence weighted evaluation metrics leveraging the uncertainty to take into account the ambiguity in our automatically generated annotations.

Method Setting Code AP AP 50 Runtime Environment
1 Mask R-CNN (Res.101) code 20.92 40.10 0.02 s 1 core @ 2.5 Ghz (C/C++)
K. He, G. Gkioxari, P. Doll\''ar and R. Girshick: Mask R-CNN. PAMI 2020.
2 Mask R-CNN (Res. 50) code 19.51 36.25 0.02 s 1 core @ 2.5 Ghz (C/C++)
K. He, G. Gkioxari, P. Doll\\\'ar and R. Girshick: Mask R-CNN. PAMI 2020.
Table as LaTeX | Only published Methods





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