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Methods › Computer Vision › Convolutional Neural Networks › HRNet

HRNet

75 papers tagged archive 2025-07-28

Introduced by Jingdong Wang et al. in Deep High-Resolution Representation Learning for Visual Recognition

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

HRNet, or High-Resolution Net, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several ($4$ in the paper) stages and the $n$th stage contains n streams corresponding to n resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over.

PaperSourceSee Code · HRNet/HRNet-Image-Classification

Papers archive 2025-07-28

30 shown of 75, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 101 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Pose Estimation28
Semantic Segmentation26
Segmentation14
Image Classification6
Image Segmentation6
Vocal Bursts Intensity Prediction6
2D Human Pose Estimation5
Decoder5
image-classification5
Autonomous Driving4
Multi-Person Pose Estimation4
Representation Learning4
3D Human Pose Estimation3
Depth Estimation3
Transfer Learning3
2D Pose Estimation2
3D Hand Pose Estimation2
Contrastive Learning2
Domain Adaptation2
Image Reconstruction2

Usage over time archive 2025-07-28

Papers per year tagged with HRNet: 2019 to 2025, peak 17 17 0 2019: 3 papers 2019 2020: 10 papers 2020 2021: 17 papers 2021 2022: 14 papers 2022 2023: 12 papers 2023 2024: 16 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (75 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Convolutional Neural Networks

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