Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks
Not on the map. Why?
Abstract (arXiv)
Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 27 Nov 2021.
Builds on
17 of 58 references.
References · 17
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
- 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
- 2020A Survey of Deep Active Learningcited by 554
- 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
- 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 463
- 2018A Survey on Deep Transfer Learningcited by 419
- 2019Confident Learning: Estimating Uncertainty in Dataset Labelscited by 413
- 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 395
- 2020A survey of loss functions for semantic segmentationcited by 217
5 more
- 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
- 2019Deep Learning vs. Traditional Computer Visioncited by 94
- 2018Open Power System Data - Frictionless data for electricity system modellingcited by 16
- 2021pyWATTS: Python Workflow Automation Tool for Time Seriescited by 9
- 2017Image Forgery Localization Based on Multi-Scale Convolutional Neural Networkscited by 8
Built on by
Citing papers per year: 2025: 1.
1 paper.