Cheap Supervision refers to a training approach where the model is trained with minimal or inexpensive labeling efforts. Instead of relying on a large amount of precisely labeled data, cheap supervision methods often leverage heuristics, weak labels, or other cost-effective annotation strategies. The goal is to reduce the manual labeling cost while still achieving acceptable performance.


Weakly But Deeply Supervised Occlusion Reasoned Parametric Layouts

Weakly But Deeply Supervised Occlusion Reasoned Parametric Layouts We propose an end to end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion reasoned layouts in perspective space as well as a parametric bird’s eye view (BEV) space. In contrast to prior works that require dense supervision such as semantic labels in perspective view, our method only requires human annotations for parametric attributes that are cheaper and less ambiguous to obtain. To solve this challenging task, our design is comprised of modules that incorporate inductive biases to learn occlusion reasoning, geometric transformation and semantic abstraction, where each module may be supervised by appropriately transforming the parametric annotations. We demonstrate how our design choices and proposed deep supervision help achieve meaningful representations and accurate predictions. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state of the art results with considerably less human supervision.