# Towards High Performance Human Keypoint Detection

arXiv: https://arxiv.org/abs/2002.00537 · HTML: https://mlsanity.com/paper/2002.00537/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 20 of 20)

- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [AP-10K: A Benchmark for Animal Pose Estimation in the Wild](https://mlsanity.com/paper/2108.12617/) (2021)
- [ViTPose++: Vision Transformer for Generic Body Pose Estimation](https://mlsanity.com/paper/2212.04246/) (2022)
- [Privacy-Preserving Portrait Matting](https://mlsanity.com/paper/2104.14222/) (2021)
- [APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking](https://mlsanity.com/paper/2206.05683/) (2022)
- [One-Shot Object Affordance Detection in the Wild](https://mlsanity.com/paper/2108.03658/) (2021)
- [Learning Visual Affordance Grounding from Demonstration Videos](https://mlsanity.com/paper/2108.05675/) (2021)
- [CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose](https://mlsanity.com/paper/2206.11752/) (2022)
- [Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification](https://mlsanity.com/paper/2108.00139/) (2021)
- [Object Localization under Single Coarse Point Supervision](https://mlsanity.com/paper/2203.09338/) (2022)
- [I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection](https://mlsanity.com/paper/2108.01343/) (2021)
- [Weakly Supervised Video Salient Object Detection via Point Supervision](https://mlsanity.com/paper/2207.07269/) (2022)
- [Learning to Purification for Unsupervised Person Re-identification](https://mlsanity.com/paper/2204.09931/) (2022)
- [SniffyArt: The Dataset of Smelling Persons](https://mlsanity.com/paper/2311.11888/) (2023)
- [Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization](https://mlsanity.com/paper/2108.09041/) (2021)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions](https://mlsanity.com/paper/2406.14367/) (2024)
- [Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose Estimation](https://mlsanity.com/paper/2502.18214/) (2025)
- [CNN-based Dendrite Core Detection from Microscopic Images of Directionally Solidified Ni-base Alloys](https://mlsanity.com/paper/2305.12506/) (2023)

## Abstract (arXiv)

Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance. In this paper, we address this problem from three aspects by devising an efficient network structure, proposing three effective training strategies, and exploiting four useful postprocessing techniques. First, we find that context information plays an important role in reasoning human body configuration and invisible keypoints. Inspired by this, we propose a cascaded context mixer (CCM), which efficiently integrates spatial and channel context information and progressively refines them. Then, to maximize CCM's representation capability, we develop a hard-negative person detection mining strategy and a joint-training strategy by exploiting abundant unlabeled data. It enables CCM to learn discriminative features from massive diverse poses. Third, we present several sub-pixel refinement techniques for postprocessing keypoint predictions to improve detection accuracy. Extensive experiments on the MS COCO keypoint detection benchmark demonstrate the superiority of the proposed method over representative state-of-the-art (SOTA) methods. Our single model achieves comparable performance with the winner of the 2018 COCO Keypoint Detection Challenge. The final ensemble model sets a new SOTA on this benchmark.