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keypoint detection

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Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 4, 2024 · Artificial Intelligence

Parsing and Visualizing COCO Keypoint Detection Annotations with Python

This tutorial explains how to explore the COCO keypoint detection annotation files, describes their JSON structure and fields, and provides step‑by‑step Python code using json, Pillow, and matplotlib to load images, extract keypoints, and draw both points and skeletal connections for visual analysis.

COCOPythonVisualization
0 likes · 12 min read
Parsing and Visualizing COCO Keypoint Detection Annotations with Python
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jun 16, 2024 · Artificial Intelligence

HRNet Source Code Walkthrough: Keypoint Dataset Construction, Online Data Augmentation, and Training Pipeline

This article provides a detailed, English-language walkthrough of the HRNet source code, covering how the COCO keypoint dataset is built, the online data‑augmentation techniques applied during training, and the end‑to‑end training and inference procedures for human pose estimation.

HRNetPyTorchcomputer vision
0 likes · 36 min read
HRNet Source Code Walkthrough: Keypoint Dataset Construction, Online Data Augmentation, and Training Pipeline
DataFunSummit
DataFunSummit
Apr 13, 2023 · Artificial Intelligence

ModelScope CV Model Overview: Visual Detection and Keypoint Applications

This article presents a comprehensive overview of ModelScope's computer‑vision models, detailing visual detection and keypoint solutions—including VitDet, YOLOX, res2net, HRNet, and 3D pose models—their architectures, performance highlights, real‑world applications, and future development plans.

AI modelsModelScopecomputer vision
0 likes · 11 min read
ModelScope CV Model Overview: Visual Detection and Keypoint Applications
Baidu Geek Talk
Baidu Geek Talk
Mar 8, 2023 · Artificial Intelligence

Understanding Motion Decomposition in AI-Based Image Animation: From Sparse to Dense Optical Flow

The article details how AI‑based image animation and face‑swapping decompose video motion into zero‑order rigid and first‑order affine components via Taylor expansion, using unsupervised U‑Net keypoint extraction, sparse-to-dense optical flow conversion, and dense motion networks that learn masks for region‑wise rigidity and non‑rigid deformation.

Taylor expansionaffine transformationface swapping
0 likes · 21 min read
Understanding Motion Decomposition in AI-Based Image Animation: From Sparse to Dense Optical Flow
Kuaishou Large Model
Kuaishou Large Model
May 13, 2021 · Artificial Intelligence

How Regressive Domain Adaptation Boosts Unsupervised Keypoint Detection

This article reviews the CVPR2021 paper on Regressive Domain Adaptation (RegDA) for unsupervised keypoint detection, explaining its motivation, novel adversarial regression framework, sparse output-space modeling, min‑min training strategy, extensive experiments, and the resulting performance gains across multiple datasets.

computer visiondomain adaptationkeypoint detection
0 likes · 13 min read
How Regressive Domain Adaptation Boosts Unsupervised Keypoint Detection
Kuaishou Large Model
Kuaishou Large Model
Dec 3, 2020 · Artificial Intelligence

Kuaishou Y‑Tech’s Real‑Time, High‑Precision Facial & Body Keypoint Detection Explained

Y‑Tech’s in‑house keypoint detection system powers Kuaishou’s beauty and effect filters across live streaming, video creation, and editing by leveraging lightweight deep‑learning models, extensive multi‑scenario data collection, and specialized handling of occlusion, enabling real‑time, robust facial and body landmark tracking on diverse mobile devices.

beauty filterscomputer visiondeep learning
0 likes · 10 min read
Kuaishou Y‑Tech’s Real‑Time, High‑Precision Facial & Body Keypoint Detection Explained
HomeTech
HomeTech
Apr 8, 2020 · Artificial Intelligence

Application of Deep Learning for Cover Image Selection in Autohome Forum Articles

This paper presents a deep learning-based approach for selecting cover images in Autohome forum articles, employing Faster R-CNN for object detection, Mask R-CNN for human keypoint detection, and MobileNetV2 for attribute recognition, achieving an overall accuracy of 81.5%.

Cover Image SelectionMobileNetcomputer vision
0 likes · 15 min read
Application of Deep Learning for Cover Image Selection in Autohome Forum Articles