activity
20182023
most citedByteTrack: Multi-Object Tracking by Associating Every Detection Box

108 citations · 150 across the 19 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CV2022★ 9 cited

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…

cs.CV2022★ 1 cited

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…

cs.CV2022

MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation

Zhenchao Jin, Dongdong Yu, Zehuan Yuan +1

Co-occurrent visual pattern makes context aggregation become an essential paradigm for semantic segmentation.The existing studies focus on modeling the contexts within image while…

cs.CV2022★ 3 cited

Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization

Xizhe Xue, Dongdong Yu, Lingqiao Liu +6

Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage se…

cs.CV2022★ 2 cited

You Should Look at All Objects

Zhenchao Jin, Dongdong Yu, Luchuan Song +2

Feature pyramid network (FPN) is one of the key components for object detectors. However, there is a long-standing puzzle for researchers that the detection performance of large-sc…

cs.CV2022

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…