Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
In this paper, we propose a method that can generate contrastive explanations for such data where we not only highlight aspects that are in themselves sufficient to justify the classification by the deep model, but also new aspects which if added will change the classification.
We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Ranked #4 on Multi-Person Pose Estimation on COCO
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars.
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Ranked #10 on Trajectory Prediction on ETH/UCY
The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w. r. t the observed environment (scene).