One-Stage Object Detection Models

CenterNet

Introduced by Duan et al. in CenterNet: Keypoint Triplets for Object Detection

CenterNet is a one-stage object detector that detects each object as a triplet, rather than a pair, of keypoints. It utilizes two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. The intuition is that, if a predicted bounding box has a high IoU with the ground-truth box, then the probability that the center keypoint in its central region is predicted as the same class is high, and vice versa. Thus, during inference, after a proposal is generated as a pair of corner keypoints, we determine if the proposal is indeed an object by checking if there is a center keypoint of the same class falling within its central region.

Source: CenterNet: Keypoint Triplets for Object Detection

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Object Detection 19 38.78%
Pose Estimation 3 6.12%
Super-Resolution 2 4.08%
Object Tracking 2 4.08%
Depth Estimation 2 4.08%
Semantic Segmentation 2 4.08%
Autonomous Vehicles 1 2.04%
Pedestrian Detection 1 2.04%
Backdoor Attack 1 2.04%

Categories