# Semi-supervised Keypoint Localization arXiv: https://arxiv.org/abs/2101.07988 ยท HTML: https://mlsanity.com/paper/2101.07988/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 35 references that are in the map. - First posted on arXiv 20 Jan 2021. ## Builds on (most cited first, 23 of 23) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020) - [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019) - [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019) - [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016) - [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017) - [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018) - [Data Distillation: Towards Omni-Supervised Learning](https://mlsanity.com/paper/1712.04440/) (2017) - [Unsupervised Learning of Object Landmarks through Conditional Image Generation](https://mlsanity.com/paper/1806.07823/) (2018) - [A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking](https://mlsanity.com/paper/1711.10378/) (2017) - [Unsupervised Discovery of Object Landmarks as Structural Representations](https://mlsanity.com/paper/1804.04412/) (2018) - [Unsupervised learning of object landmarks by factorized spatial embeddings](https://mlsanity.com/paper/1705.02193/) (2017) - [Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors](https://mlsanity.com/paper/1807.00966/) (2018) - [Improving Landmark Localization with Semi-Supervised Learning](https://mlsanity.com/paper/1709.01591/) (2017) - [Unsupervised Learning of Landmarks by Descriptor Vector Exchange](https://mlsanity.com/paper/1908.06427/) (2019) - [Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection](https://mlsanity.com/paper/1908.02116/) (2019) - [Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification](https://mlsanity.com/paper/1912.01300/) (2019) - [Keypoint-Aligned Embeddings for Image Retrieval and Re-identification](https://mlsanity.com/paper/2008.11368/) (2020) - [Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition](https://mlsanity.com/paper/1801.09057/) (2018) - [Semi- and Weakly-supervised Human Pose Estimation](https://mlsanity.com/paper/1906.01399/) (2019) - [ATRW: A Benchmark for Amur Tiger Re-identification in the Wild](https://mlsanity.com/paper/1906.05586/) (2019) ## Built on by (most cited first, 15 of 15) - [Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation](https://mlsanity.com/paper/2206.12772/) (2022) - [Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose](https://mlsanity.com/paper/2110.14213/) (2021) - [Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization](https://mlsanity.com/paper/2201.08613/) (2022) - [Dynamic Curriculum Learning for Great Ape Detection in the Wild](https://mlsanity.com/paper/2205.00275/) (2022) - [One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement](https://mlsanity.com/paper/2208.00453/) (2022) - [Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection](https://mlsanity.com/paper/2303.05269/) (2023) - [Few-shot Geometry-Aware Keypoint Localization](https://mlsanity.com/paper/2303.17216/) (2023) - [Learning Articulated Shape with Keypoint Pseudo-labels from Web Images](https://mlsanity.com/paper/2304.14396/) (2023) - [Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training](https://mlsanity.com/paper/2112.13709/) (2021) - [Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap](https://mlsanity.com/paper/2107.08653/) (2021) - [Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency](https://mlsanity.com/paper/2107.08639/) (2021) - [Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation](https://mlsanity.com/paper/2310.00099/) (2023) - [Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training](https://mlsanity.com/paper/2402.11566/) (2024) - [Incremental Object Keypoint Learning](https://mlsanity.com/paper/2503.20248/) (2025) - [Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection](https://mlsanity.com/paper/2507.07994/) (2025) ## Abstract (arXiv) Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that exhibit large variations in poses that greatly influence their visual appearance, such as wild animals. However, supervised training of a keypoint detection network requires annotating a large image dataset for each animal species, which is a labor-intensive task. To reduce the need for labeled data, we propose to learn simultaneously keypoint heatmaps and pose invariant keypoint representations in a semi-supervised manner using a small set of labeled images along with a larger set of unlabeled images. Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset. Pose invariance is achieved by making keypoint representations for the image and its augmented copies closer together in feature space. Our semi-supervised approach significantly outperforms previous methods on several benchmarks for human and animal body landmark localization.