108 citations · 181 across the 21 of their papers we have counts for
23 papers
Box-Level Active Detection
Mengyao Lyu, Jundong Zhou, Hui Chen +7
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchma…
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…
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…
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…
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…
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…
AdaptivePose: Human Parts as Adaptive Points
Yabo Xiao, Xiaojuan Wang, Dongdong Yu +3
Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation…
Trimap-guided Feature Mining and Fusion Network for Natural Image Matting
Weihao Jiang, Dongdong Yu, Zhaozhi Xie +3
Utilizing trimap guidance and fusing multi-level features are two important issues for trimap-based matting with pixel-level prediction. To utilize trimap guidance, most existing a…
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Yifu Zhang, Peize Sun, Yi Jiang +6
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are hi…
Weakly Supervised Person Search with Region Siamese Networks
Chuchu Han, Kai Su, Dongdong Yu +5
Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to colle…
Memory Based Video Scene Parsing
Zhenchao Jin, Dongdong Yu, Kai Su +2
Video scene parsing is a long-standing challenging task in computer vision, aiming to assign pre-defined semantic labels to pixels of all frames in a given video. Compared with ima…
Mining Contextual Information Beyond Image for Semantic Segmentation
Zhenchao Jin, Tao Gong, Dongdong Yu +4
This paper studies the context aggregation problem in semantic image segmentation. The existing researches focus on improving the pixel representations by aggregating the contextua…
Body Meshes as Points
Jianfeng Zhang, Dongdong Yu, Jun Hao Liew +2
We consider the challenging multi-person 3D body mesh estimation task in this work. Existing methods are mostly two-stage based--one stage for person localization and the other sta…
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
Guanghao Yin, Wei Wang, Zehuan Yuan +5
Although single-image super-resolution (SISR) methods have achieved great success on single degradation, they still suffer performance drop with multiple degrading effects in real…
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
Daizong Liu, Dongdong Yu, Changhu Wang +1
Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearanc…
SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation
Yabo Xiao, Dongdong Yu, Xiaojuan Wang +3
Human pose estimation is a fundamental yet challenging task in computer vision. Although deep learning techniques have made great progress in this area, difficult scenarios (e.g.,…
Towards Good Practices for Instance Segmentation
Dongdong Yu, Zehuan Yuan, Jinlai Liu +2
Instance Segmentation is an interesting yet challenging task in computer vision. In this paper, we conduct a series of refinements with the Hybrid Task Cascade (HTC) Network, and e…
Towards Good Practices for Multi-Person Pose Estimation
Dongdong Yu, Kai Su, Changhu Wang
Multi-Person Pose Estimation is an interesting yet challenging task in computer vision. In this paper, we conduct a series of refinements with the MSPN and PoseFix Networks, and em…
Towards Good Practices for Video Object Segmentation
Dongdong Yu, Kai Su, Hengkai Guo +6
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based vi…
A Context-and-Spatial Aware Network for Multi-Person Pose Estimation
Dongdong Yu, Kai Su, Xin Geng +1
Multi-person pose estimation is a fundamental yet challenging task in computer vision. Both rich context information and spatial information are required to precisely locate the ke…
Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
Kai Su, Dongdong Yu, Zhenqi Xu +2
Multi-person pose estimation is an important but challenging problem in computer vision. Although current approaches have achieved significant progress by fusing the multi-scale fe…
Mask Propagation Network for Video Object Segmentation
Jia Sun, Dongdong Yu, Yinghong Li +1
In this work, we propose a mask propagation network to treat the video segmentation problem as a concept of the guided instance segmentation. Similar to most MaskTrack based video…