Search Results for author:
Found 5 papers, 4 papers with code
CenterNet++ for Object Detection
Our approach, named CenterNet, detects each object as a triplet keypoints (top-left and bottom-right corners and the center keypoint).
Ranked #35 on
Object Detection
on COCO test-dev
Location-Sensitive Visual Recognition with Cross-IOU Loss
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks.
Ranked #45 on
Object Detection
on COCO test-dev
Corner Proposal Network for Anchor-free, Two-stage Object Detection
On the MS-COCO dataset, CPN achieves an AP of 49. 2% which is competitive among state-of-the-art object detection methods.
Ranked #83 on
Object Detection
on COCO test-dev
CenterNet: Keypoint Triplets for Object Detection
In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions.
Ranked #105 on
Object Detection
on COCO test-dev
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.
Ranked #5 on
Object Detection
on UAVDT
