On the public citation graph

GLPose: Global-Local Representation Learning for Human Pose Estimation

https://doi.org/10.1145/3519305

0 signed · 72 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01231-1_29openalex-snapshot

Simple Baselines for Human Pose Estimation and Tracking

inferredcites →https://doi.org/10.1007/978-3-030-58520-4_36openalex-snapshot

Key Frame Proposal Network for Efficient Pose Estimation in Videos

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/978-3-319-46484-8_29openalex-snapshot

Stacked Hourglass Networks for Human Pose Estimation

inferredcites →https://doi.org/10.1007/978-3-540-88690-7_53openalex-snapshot

Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation

inferredcites →https://doi.org/10.1007/978-3-642-15552-9_30openalex-snapshot

Cascaded Models for Articulated Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr.2012.6248079openalex-snapshot

Conditional regression forests for human pose estimation

inferredcites →https://doi.org/10.1109/cvpr.2013.391openalex-snapshot

Human Pose Estimation Using Body Parts Dependent Joint Regressors

inferredcites →https://doi.org/10.1109/cvpr.2013.83openalex-snapshot

Beyond Physical Connections: Tree Models in Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr.2014.214openalex-snapshot

DeepPose: Human Pose Estimation via Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2015.7298734openalex-snapshot

Joint action recognition and pose estimation from video

inferredcites →https://doi.org/10.1109/cvpr.2016.334openalex-snapshot

Personalizing Human Video Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr.2016.511openalex-snapshot

Convolutional Pose Machines

inferredcites →https://doi.org/10.1109/cvpr.2016.512openalex-snapshot

Human Pose Estimation with Iterative Error Feedback

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.143openalex-snapshot

Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

inferredcites →https://doi.org/10.1109/cvpr.2017.590openalex-snapshot

Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos

inferredcites →https://doi.org/10.1109/cvpr.2017.601openalex-snapshot

Multi-context Attention for Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr.2018.00044openalex-snapshot

Detect-and-Track: Efficient Pose Estimation in Videos

inferredcites →https://doi.org/10.1109/cvpr.2018.00542openalex-snapshot

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

inferredcites →https://doi.org/10.1109/cvpr.2018.00546openalex-snapshot

LSTM Pose Machines

inferredcites →https://doi.org/10.1109/cvpr.2019.00475openalex-snapshot

Efficient Online Multi-Person 2D Pose Tracking With Recurrent Spatio-Temporal Affinity Fields

inferredcites →https://doi.org/10.1109/cvpr.2019.00581openalex-snapshot

Multi-Person Articulated Tracking With Spatial and Temporal Embeddings

inferredcites →https://doi.org/10.1109/cvpr.2019.00582openalex-snapshot

Multi-Person Pose Estimation With Enhanced Channel-Wise and Spatial Information

inferredcites →https://doi.org/10.1109/cvpr.2019.00584openalex-snapshot

Deep High-Resolution Representation Learning for Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr.2019.00678openalex-snapshot

Learning to Compose Dynamic Tree Structures for Visual Contexts

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00543openalex-snapshot

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00706openalex-snapshot

UniPose: Unified Human Pose Estimation in Single Images and Videos

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00712openalex-snapshot

Distribution-Aware Coordinate Representation for Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01035openalex-snapshot

Memory Enhanced Global-Local Aggregation for Video Object Detection

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01110openalex-snapshot

Combining Detection and Tracking for Human Pose Estimation in Videos

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01310openalex-snapshot

Mixture Dense Regression for Object Detection and Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00059openalex-snapshot

Deep Dual Consecutive Network for Human Pose Estimation

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00251openalex-snapshot

Unsupervised Human Pose Estimation through Transforming Shape Templates

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00798openalex-snapshot

Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking

inferredcites →https://doi.org/10.1109/fg.2017.61openalex-snapshot

Pose for Action - Action for Pose

inferredcites →https://doi.org/10.1109/iccv.2009.5459306openalex-snapshot

Efficient human pose estimation via parsing a tree structure based human model

inferredcites →https://doi.org/10.1109/iccv.2011.6126552openalex-snapshot

N-best maximal decoders for part models

inferredcites →https://doi.org/10.1109/iccv.2015.222openalex-snapshot

Flowing ConvNets for Human Pose Estimation in Videos

inferredcites →https://doi.org/10.1109/iccv.2017.256openalex-snapshot

RMPE: Regional Multi-person Pose Estimation

inferredcites →https://doi.org/10.1109/iccv.2019.00704openalex-snapshot

Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

inferredcites →https://doi.org/10.1109/iccv.2019.00931openalex-snapshot

Sequence Level Semantics Aggregation for Video Object Detection

inferredcites →https://doi.org/10.1109/iccv48922.2021.01084openalex-snapshot

Human Pose Regression with Residual Log-likelihood Estimation

inferredcites →https://doi.org/10.1109/iccv48922.2021.01151openalex-snapshot

Motion Adaptive Pose Estimation from Compressed Videos

inferredcites →https://doi.org/10.1109/ijcnn.2019.8851734openalex-snapshot

Pose estimator and tracker using temporal flow maps for limbs

inferredcites →https://doi.org/10.1109/tip.2017.2765836openalex-snapshot

Person Re-Identification With Metric Learning Using Privileged Information

inferredcites →https://doi.org/10.1109/tip.2020.3026625openalex-snapshot

Iterative Local-Global Collaboration Learning Towards One-Shot Video Person Re-Identification

inferredcites →https://doi.org/10.1109/tmm.2020.2980194openalex-snapshot

Pose-Guided Tracking-by-Detection: Robust Multi-Person Pose Tracking

inferredcites →https://doi.org/10.1109/tpami.2021.3139918openalex-snapshot

Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction

inferredcites →https://doi.org/10.1145/3240508.3240549openalex-snapshot

Cross-modal Moment Localization in Videos

inferredcites →https://doi.org/10.1145/3394171.3416299openalex-snapshot

Towards Accurate Human Pose Estimation in Videos of Crowded Scenes

inferredcites →https://doi.org/10.1145/3474085.3475218openalex-snapshot

Selective Dependency Aggregation for Action Classification

inferredcites →https://doi.org/10.1145/3474085.3475450openalex-snapshot

A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval

inferredcites →https://doi.org/10.48550/arxiv.1805.04596openalex-snapshot

Joint Flow: Temporal Flow Fields for Multi Person Tracking

inferredcites →https://doi.org/10.48550/arxiv.1812.09899openalex-snapshot

Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation

inferredcites →https://doi.org/10.48550/arxiv.1908.05593openalex-snapshot

FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks

inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot

Advances in Neural Information Processing Systems 14

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s00521-023-08844-yopenalex-snapshot

JointMETRO: a 3D reconstruction model for human figures in works of art based on transformer

inferred← citeshttps://doi.org/10.1007/s10462-025-11430-4openalex-snapshot

A survey on deep learning for 2D and 3D human pose estimation

inferred← citeshttps://doi.org/10.1016/j.aej.2026.02.027openalex-snapshot

PCC-guided transformer with keypoint-based interaction and dynamic region-sensitive for human pose estimation

inferred← citeshttps://doi.org/10.1016/j.dcan.2024.03.007openalex-snapshot

Spatiotemporal correlation based self-adaptive pose estimation in complex scenes

inferred← citeshttps://doi.org/10.1016/j.patcog.2025.112193openalex-snapshot

Video-based human pose estimation via feature decoupling and multi-hypothesis calibration

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3269666openalex-snapshot

Spatiotemporal Learning Transformer for Video-Based Human Pose Estimation

inferred← citeshttps://doi.org/10.1145/3628432openalex-snapshot

Sparsity-guided Discriminative Feature Encoding for Robust Keypoint Detection

inferred← citeshttps://doi.org/10.1145/3639707openalex-snapshot

Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video

inferred← citeshttps://doi.org/10.1145/3695877openalex-snapshot

Category-Level Pose Estimation and Iterative Refinement for Monocular RGB-D Image

inferred← citeshttps://doi.org/10.1145/3716387openalex-snapshot

Multi-Anchor Offset Representation Based Coarse-to-Fine Diffusion Model for Human Pose Estimation

inferred← citeshttps://doi.org/10.1145/3800573openalex-snapshot

SCALE-Pose: Skeletal Correction and Language Knowledge-assisted for 3D Human Pose Estimation

inferred← citeshttps://doi.org/10.3390/e25010020openalex-snapshot

CSI-Former: Pay More Attention to Pose Estimation with WiFi

inferred← citeshttps://doi.org/10.3390/make5040081openalex-snapshot

Human Pose Estimation Using Deep Learning: A Systematic Literature Review

inferred← citeshttps://doi.org/10.56726/irjmets51313openalex-snapshot

ADVANCING YOGA PRACTICE : LEVERAGING OPENCV AND MEDIAPIPE FOR POSE DETECTION

inferred← citeshttps://doi.org/10.7717/peerj-cs.3100openalex-snapshot

Dense symmetric temporal alignment learning for human pose estimation

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.