29 papers with code • 2 benchmarks • 11 datasets
We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
It can easily be combined with earlier approaches to use PAM and CLARA on big data (some of which use PAM as a subroutine, hence can immediately benefit from these improvements), where the performance with high k becomes increasingly important.
In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation.
Ranked #4 on Vehicle Pose Estimation on KITTI Cars Hard
We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension.
Object detection in camera images, using deep learning has been proven successfully in recent years.