Search results
info@cocodataset.org. Home; People
The COCO Object Detection Task is designed to push the state of the art in object detection forward. Teams are encouraged to compete in either (or both) of two object detection challenges: using bounding box output or object segmentation output.
Aug 23, 2020 · COCO is a widely used visual recognition dataset, designed to spur object detection research with a focus on full scene understanding. In particular: detecting non-iconic views of objects, localizing objects in images with pixel level precision, and detection of objects in complex scenes.
What is COCO? COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation; Recognition in context; Superpixel stuff segmentation; 330K images (>200K labeled) 1.5 million object instances; 80 object categories; 91 stuff categories; 5 captions per image; 250,000 people with keypoints
Oct 27, 2019 · COCO is an image dataset designed to spur object detection research with a focus on detecting objects in context. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image.
pose estimation problems in COCO dataset, including heavily occlusion, large variance and crowding cases. •Hourglass shows great performance for single pose
object detection by adding a separate branch for mask pre-diction, thereby predicting pixel level object masks in par-allel to predicting the bounding box positions of objects. The HTC framework offers a special cascaded combination of R-CNN and gives the best performance in 2018 COCO object detection challenge1. In semantic segmentation, we
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Detection Challenge Track Technical Report: ByteDance HRNet Bin Xiao, Zaizhou Gong, Yifan Lu, Linfu Wen ByteDance AI Lab Abstract In this report, we present our multi-person keypoint de-tection system for COCO Keypoint Detection Challenge 2019. It contains three main components ...
Aware Localization is proposed to locate objects more ac-curately. In particular, we reformulate object localization as a task to localize the four edges of the bounding box for each object, and design a novel pipeline to perform coarse estimation and fine regression. Our overall sys-tem achieves 51.3% mask mAP on the COCO test-dev split,
Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable • each object detected and segmented separately • “stuff” is not segmented
