# A survey of image labelling for computer vision applications

arXiv: https://arxiv.org/abs/2104.08885 · HTML: https://mlsanity.com/paper/2104.08885/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 95 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images](https://mlsanity.com/paper/1909.04349/) (2019)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Superpixels: An Evaluation of the State-of-the-Art](https://mlsanity.com/paper/1612.01601/) (2016)
- [labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/2103.04970/) (2021)
- [Interactive Image Segmentation using Label Propagation through Complex Networks](https://mlsanity.com/paper/1901.02573/) (2019)
- [Automatic Image Annotation via Label Transfer in the Semantic Space](https://mlsanity.com/paper/1605.04770/) (2016)
- [Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images](https://mlsanity.com/paper/1611.09162/) (2016)
- [Semi-automated labelling of medical images: benefits of a collaborative work in the evaluation of prostate cancer in MRI](https://mlsanity.com/paper/1708.08698/) (2017)
- [Learning Image Conditioned Label Space for Multilabel Classification](https://mlsanity.com/paper/1802.07460/) (2018)
- [VISIR: Visual and Semantic Image Label Refinement](https://mlsanity.com/paper/1909.00741/) (2019)

## Built on by (most cited first, 18 of 18)

- [ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO](https://mlsanity.com/paper/2204.03359/) (2022)
- [Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling](https://mlsanity.com/paper/2401.08876/) (2024)
- [SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input](https://mlsanity.com/paper/2411.11934/) (2024)
- [Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts](https://mlsanity.com/paper/2303.17595/) (2023)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data](https://mlsanity.com/paper/2409.00048/) (2024)
- [Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation](https://mlsanity.com/paper/2306.12189/) (2023)
- [LiDAR-based curb detection for ground truth annotation in automated driving validation](https://mlsanity.com/paper/2312.00534/) (2023)
- [It's Not Just Labeling -- A Research on LLM Generated Feedback Interpretability and Image Labeling Sketch Features](https://mlsanity.com/paper/2505.19419/) (2025)
- [QualitEye: Public and Privacy-preserving Gaze Data Quality Verification](https://mlsanity.com/paper/2506.05908/) (2025)
- [A Light in the Dark: Deep Learning Practices for Industrial Computer Vision](https://mlsanity.com/paper/2201.02028/) (2022)
- [BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis](https://mlsanity.com/paper/2308.08003/) (2023)
- [Exploring Different Levels of Supervision for Detecting and Localizing Solar Panels on Remote Sensing Imagery](https://mlsanity.com/paper/2309.10421/) (2023)
- [Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate](https://mlsanity.com/paper/2406.07583/) (2024)
- [Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review](https://mlsanity.com/paper/2407.00252/) (2024)
- [The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models](https://mlsanity.com/paper/2505.14964/) (2025)
- [Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization](https://mlsanity.com/paper/2512.16504/) (2025)
- [From Bounding Boxes to Visual Reasoning: An On-Policy Data Annotation Tool for Vision-Language Models](https://mlsanity.com/paper/2606.18846/) (2026)

## Abstract (arXiv)

Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.