# Improving Landmark Localization with Semi-Supervised Learning

arXiv: https://arxiv.org/abs/1709.01591 · HTML: https://mlsanity.com/paper/1709.01591/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 30 of 39)

- [Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation](https://mlsanity.com/paper/2206.12772/) (2022)
- [ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment](https://mlsanity.com/paper/2109.05721/) (2021)
- [Supervision by Registration and Triangulation for Landmark Detection](https://mlsanity.com/paper/2101.09866/) (2021)
- [Pre-training strategies and datasets for facial representation learning](https://mlsanity.com/paper/2103.16554/) (2021)
- [Distance-aware Quantization](https://mlsanity.com/paper/2108.06983/) (2021)
- [Precise Facial Landmark Detection by Reference Heatmap Transformer](https://mlsanity.com/paper/2303.07840/) (2023)
- [Semi-supervised Keypoint Localization](https://mlsanity.com/paper/2101.07988/) (2021)
- [Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation](https://mlsanity.com/paper/2107.06941/) (2021)
- [Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization](https://mlsanity.com/paper/2201.08613/) (2022)
- [Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling](https://mlsanity.com/paper/2104.04323/) (2021)
- [Hopper: Multi-hop Transformer for Spatiotemporal Reasoning](https://mlsanity.com/paper/2103.10574/) (2021)
- [Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training](https://mlsanity.com/paper/2105.13593/) (2021)
- [Localization with Sampling-Argmax](https://mlsanity.com/paper/2110.08825/) (2021)
- [Unsupervised Multi-object Segmentation Using Attention and Soft-argmax](https://mlsanity.com/paper/2205.13271/) (2022)
- [Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos](https://mlsanity.com/paper/2305.03713/) (2023)
- [One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement](https://mlsanity.com/paper/2208.00453/) (2022)
- [OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection](https://mlsanity.com/paper/2409.19899/) (2024)
- [GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data](https://mlsanity.com/paper/2207.13297/) (2022)
- [Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network](https://mlsanity.com/paper/2112.12328/) (2021)
- [Few-shot Geometry-Aware Keypoint Localization](https://mlsanity.com/paper/2303.17216/) (2023)
- [Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment](https://mlsanity.com/paper/1910.09450/) (2019)
- [Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal](https://mlsanity.com/paper/2104.09457/) (2021)
- [Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph](https://mlsanity.com/paper/2104.14629/) (2021)
- [When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model](https://mlsanity.com/paper/2105.13150/) (2021)
- [Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training](https://mlsanity.com/paper/2112.13709/) (2021)
- [ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints](https://mlsanity.com/paper/2210.09204/) (2022)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation](https://mlsanity.com/paper/2311.01929/) (2023)
- [Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach](https://mlsanity.com/paper/2404.04556/) (2024)
- [Multi-Domain Multi-Definition Landmark Localization for Small Datasets](https://mlsanity.com/paper/2203.10358/) (2022)

## Abstract (arXiv)

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data. A key aspect of our approach is that errors can be backpropagated through a complete landmark localization model. Second, we propose and explore an unsupervised learning technique for landmark localization based on having a model predict equivariant landmarks with respect to transformations applied to the image. We show that these techniques, improve landmark prediction considerably and can learn effective detectors even when only a small fraction of the dataset has landmark labels. We present results on two toy datasets and four real datasets, with hands and faces, and report new state-of-the-art on two datasets in the wild, e.g. with only 5% of labeled images we outperform previous state-of-the-art trained on the AFLW dataset.