# Pseudo Pixel-level Labeling for Images with Evolving Content

arXiv: https://arxiv.org/abs/2105.09975 · HTML: https://mlsanity.com/paper/2105.09975/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations](https://mlsanity.com/paper/1904.05044/) (2019)
- [FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference](https://mlsanity.com/paper/1902.10421/) (2019)
- [Weakly Supervised Instance Segmentation using Class Peak Response](https://mlsanity.com/paper/1804.00880/) (2018)
- [Deep Metric Transfer for Label Propagation with Limited Annotated Data](https://mlsanity.com/paper/1812.08781/) (2018)
- [Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation](https://mlsanity.com/paper/1806.07527/) (2018)
- [Block Annotation: Better Image Annotation for Semantic Segmentation with Sub-Image Decomposition](https://mlsanity.com/paper/2002.06626/) (2020)

## Abstract (arXiv)

Annotating images for semantic segmentation requires intense manual labor and is a time-consuming and expensive task especially for domains with a scarcity of experts, such as Forensic Anthropology. We leverage the evolving nature of images depicting the decay process in human decomposition data to design a simple yet effective pseudo-pixel-level label generation technique to reduce the amount of effort for manual annotation of such images. We first identify sequences of images with a minimum variation that are most suitable to share the same or similar annotation using an unsupervised approach. Given one user-annotated image in each sequence, we propagate the annotation to the remaining images in the sequence by merging it with annotations produced by a state-of-the-art CAM-based pseudo label generation technique. To evaluate the quality of our pseudo-pixel-level labels, we train two semantic segmentation models with VGG and ResNet backbones on images labeled using our pseudo labeling method and those of a state-of-the-art method. The results indicate that using our pseudo-labels instead of those generated using the state-of-the-art method in the training process improves the mean-IoU and the frequency-weighted-IoU of the VGG and ResNet-based semantic segmentation models by 3.36%, 2.58%, 10.39%, and 12.91% respectively.