For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without fine-tuning.
For autonomous driving, this means that large objects close to the sensors are easily visible, but far-away or small objects comprise only one measurement or two.
Ranked #8 on 3D Object Detection on nuScenes
We develop a probabilistic interpretation of two-stage object detection.
Ranked #16 on Object Detection on COCO test-dev (using extra training data)
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
Ranked #1 on 3D Object Detection on waymo pedestrian
Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
Ranked #2 on Multiple Object Tracking on KITTI Tracking test
With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem.
Ranked #80 on Object Detection on COCO minival
Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices.
Ranked #2 on Keypoint Detection on Pascal3D+
In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image.
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation.