Semantic Segmentation on Multiple Visual Domains
This addresses the challenge of scarce and expensive pixel-level annotations for semantic segmentation by enabling multi-domain training to increase label-space, though it is incremental as it builds on existing multi-domain research.
The paper tackles the problem of semantic segmentation models being limited to single domains by proposing a method to train on three non-overlapping visual domains (Cityscapes, SUIM, and SUN RGB-D) without manual labeling, resulting in higher accuracy than baseline models when hardware performance is equalized.
Semantic segmentation models only perform well on the domain they are trained on and datasets for training are scarce and often have a small label-spaces, because the pixel level annotations required are expensive to make. Thus training models on multiple existing domains is desired to increase the output label-space. Current research shows that there is potential to improve accuracy across datasets by using multi-domain training, but this has not yet been successfully extended to datasets of three different non-overlapping domains without manual labelling. In this paper a method for this is proposed for the datasets Cityscapes, SUIM and SUN RGB-D, by creating a label-space that spans all classes of the datasets. Duplicate classes are merged and discrepant granularity is solved by keeping classes separate. Results show that accuracy of the multi-domain model has higher accuracy than all baseline models together, if hardware performance is equalized, as resources are not limitless, showing that models benefit from additional data even from domains that have nothing in common.