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Found 6 papers, 0 papers with code
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3. 5 seconds before they occur in real-time with a precision and recall of 90. 5\% and 87. 4\% respectively.
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
We introduce a sensory-fusion architecture which jointly learns to anticipate and fuse information from multiple sensory streams.
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3. 5 seconds before they occur with over 80\% F1-score in real-time.
RoboBrain: Large-Scale Knowledge Engine for Robots
In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks.
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation
However, because of the ambiguity in the temporal segmentation of the sub-activities that constitute an activity, in the past as well as in the future, multiple graph structures are possible.
Ranked #2 on
Skeleton Based Action Recognition
on CAD-120
Semantic Labeling of 3D Point Clouds for Indoor Scenes
In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views, having 2495 segments labeled with 27 object classes), we get a performance of 84. 06% in labeling 17 object classes for offices, and 73. 38% in labeling 17 object classes for home scenes.
