Search Results for author:
Found 17 papers, 4 papers with code
Learning Quality-aware Representation for Multi-person Pose Regression
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 for selecting the pose candidates.
AdaptivePose: Human Parts as Adaptive Points
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 cost and low efficiency.
Trimap-guided Feature Mining and Fusion Network for Natural Image Matting
For emerging content-based feature fusion, most existing matting methods only focus on local features which lack the guidance of a global feature with strong semantic information related to the interesting object.
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Ranked #1 on
Multi-Object Tracking
on MOT17
(using extra training data)
Weakly Supervised Person Search with Region Siamese Networks
Large-scale labeled training data is often difficult to collect, especially for person identities.
Memory Based Video Scene Parsing
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.
Mining Contextual Information Beyond Image for Semantic Segmentation
To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations.
Body Meshes as Points
In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet).
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
Specifically, our proposed network consists of three main parts: Siamese Encoder Module, Center Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module.
SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation
To alleviate these issues, we propose a novel Spatial Preserve and Content-aware Network(SPCNet), which includes two effective modules: Dilated Hourglass Module(DHM) and Selective Information Module(SIM).
Towards Good Practices for Instance Segmentation
Instance Segmentation is an interesting yet challenging task in computer vision.
Towards Good Practices for Multi-Person Pose Estimation
Multi-Person Pose Estimation is an interesting yet challenging task in computer vision.
Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
One-shot visual object segmentation
Semantic Segmentation
+1
A Context-and-Spatial Aware Network for Multi-Person Pose Estimation
In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information.
Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
Multi-person pose estimation is an important but challenging problem in computer vision.
Mask Propagation Network for Video Object Segmentation
In this work, we propose a mask propagation network to treat the video segmentation problem as a concept of the guided instance segmentation.
