A bottom-up pose estimator built by adding a deconvolution-based high-resolution feature pyramid to HRNet, trained with multi-resolution supervision and tested with heatmap aggregation, reports state-of-the-art COCO and CrowdPose results.
Panoptic-DeepLab
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We present Panoptic-DeepLab, a bottom-up and single-shot approach for panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers state-of-the-art results. In particular, we adopt the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. Our single Panoptic-DeepLab sets the new state-of-art at all three Cityscapes benchmarks, reaching 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set, and advances results on the other challenging Mapillary Vistas.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2019 1verdicts
ACCEPT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
A bottom-up pose estimator built by adding a deconvolution-based high-resolution feature pyramid to HRNet, trained with multi-resolution supervision and tested with heatmap aggregation, reports state-of-the-art COCO and CrowdPose results.