Recent semantic segmentation systems have achieved significant improvement by performing pixel-wise training with hierarchical features using deep convolutional neural network models. While the learning process usually requires pixel-level annotated images, it is difficult to get desirable amounts of fine-labeled data and thus the training set size is more likely to be limited, often in thousands. This means that top methods for a dataset can be fine-tuned for a specific situation, making the generalization ability unclear. In real-world applications like self-driving systems, ambiguous region or lack of context information can cause errors in the predicted results. Resolving such ambiguities is crucial for subsequent operations to be performed safely.