Problem Statement

There are three problems we had to solve in our project, including the generalization of the Model, and the data set labeling:

0 1-M A N U A L L A B E L I N G OF L A R G E D A T A S E T S :

The supervised approach requires a lot of annotated data. The pixel-level labeling of large datasets [such as Cityscapes] is extremely costly due to the amount of human effort required [Manual Labeling]. Like in Figure 3.a.

02-D E A L I N G W I T H D I F F E R EN T C O N T E X T S I N S E L F-D R I V I N G C A R S:

In the context of self-driving cars, the perception system is often put to test in various scenarios including different cities, weathers, or lighting conditions. There is a distribution shift under different contexts, as we see in Figure3.b, the source is from one Domain and multi target. So this will be a problem if the model is ungeneralizable.

0 3-L I T E R A T U R E D E A L S W I T H M U L T I P L E C O N T E X T S A S A S I N G L E-T A R G E T :

Most previous works address the single-target setting whose goal is to adapt from source to a particular target domain of interest. To deal with multiple test distributions, one can adopt single-target techniques by either:

 Training multiple models for all target domains and adaptively activating one at test time  This Strategy raises storage issues for embedded platforms.  Merging all target data and treating them as being drawn from a single target distribution.  This Strategy overlooks distribution shifts across different target domains.

Fig3.a

Fig3.b