Deep learning-based approaches have achieved great success in the semantic segmentation [24, 43, 2, 7, 42, 3, 17, 10], thanks to a large amount of fully annotated data. However, collecting large-scale accurate pixel-level annotations can be extremely time and cost consuming [6]. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which groundtruth annotations are generated automatically [33, 34, 32]. Unfortunately, models trained purely on simulated data often fail to generalize to the real world due to thedomain shifts.

With a separately trained critic model that selects high-quality examples, we find that training on the localized commonsense corpus can successfully distill existing VL models to support a reference-as-input interface.

DePT induces a cone-shaped spatial-temporal attention prior,which injects theinformation propagation and aggregation principles and enables a global view. With physical constraint inductive bias baked into its design, our DePT is ready to plug and play for a broad class of multi-agent systems. The experimental results on one of the most challenging CPS - network-scale traffic signal control system in the open world - show that our model outperformed the state-of-the-art expert methods on synthetic and real-world datasets.

Furthermore, we develop a method to eliminate bias in cases where blindly assuming IID is expected to yield a significantly biased estimate.