11 citations · 21 across the 4 of their papers we have counts for
4 papers
Single-stage Multi-human Parsing via Point Sets and Center-based Offsets
Jiaming Chu, Lei Jin, Junliang Xing +1
This work studies the multi-human parsing problem. Existing methods, either following top-down or bottom-up two-stage paradigms, usually involve expensive computational costs. We i…
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
Yabo Xiao, Kai Su, Xiaojuan Wang +4
We propose a sparse end-to-end multi-person pose regression framework, termed QueryPose, which can directly predict multi-person keypoint sequences from the input image. The existi…
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
Yabo Xiao, Xiaojuan Wang, Dongdong Yu +5
Multi-person pose estimation generally follows top-down and bottom-up paradigms. Both of them use an extra stage ( human detection in top-down paradigm or group…
Learning Quality-aware Representation for Multi-person Pose Regression
Yabo Xiao, Dongdong Yu, Xiaojuan Wang +3
Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i.e., confidence of the instance localization) to indicate the pose quality f…