Search Results for author:
Found 24 papers, 12 papers with code
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization.
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search
Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i. e., pedestrian detection and person re-identification.
Ranked #3 on
Person Search
on PRW
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose.
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation
To this end, we propose a novel soft mining contextual information beyond image paradigm named MCIBI++ to further boost the pixel-level representations.
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss.
You Should Look at All Objects
Feature pyramid network (FPN) is one of the key components for object detectors.
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.
Ranked #4 on
Image Matting
on Composition-1K
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
Multiple Object Tracking
on BDD100K val
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.
Ranked #9 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Shape Estimation
3D Multi-Person Pose Estimation
+1
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.
Ranked #6 on
Unsupervised Video Object Segmentation
on FBMS test
Semantic Segmentation
Unsupervised Video Object Segmentation
+1
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 Multi-Person Pose Estimation
Multi-Person Pose Estimation is an interesting yet challenging task in computer vision.
Towards Good Practices for Instance Segmentation
Instance Segmentation 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.
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.
