Semanic Scene Understanding

2D Semantic Segmentation


Our evaluation table ranks all methods according to the confidence weighted mean intersection-over-union (mIoU). The weighted IoU of one class can be defined 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 the class label, 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 mIoU Class mIoU Category Runtime Environment
1 PSPNet code 64.92 82.17 0.2 s 1 core @ 2.5 Ghz (C/C++)
H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia: Pyramid Scene Parsing Network. CVPR 2017.
2 FCN 54.00 77.64 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Long, E. Shelhamer and T. Darrell: Fully Convolutional Networks for Semantic Segmentation. CVPR 2015.
Table as LaTeX | Only published Methods





eXTReMe Tracker