Search Results for author:
Found 4 papers, 3 papers with code
DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding
The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.
Learning to Predict Navigational Patterns from Partial Observations
We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field.
Ranked #1 on
Lane Detection
on nuScenes
Predictive World Models from Real-World Partial Observations
By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications.
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.
