SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images
This work addresses a domain-specific challenge in forensic anthropology by improving segmentation accuracy with limited labeled data, though it is incremental as it builds on existing semi-supervised techniques.
The paper tackles the problem of semantic segmentation for human decomposition images, where pixel-level annotation is costly and expert-scarce, by proposing a semi-supervised method that reuses labels from similar images with dynamic weighting, and it outperforms state-of-the-art methods on this dataset.
Semantic segmentation is a challenging computer vision task demanding a significant amount of pixel-level annotated data. Producing such data is a time-consuming and costly process, especially for domains with a scarcity of experts, such as medicine or forensic anthropology. While numerous semi-supervised approaches have been developed to make the most from the limited labeled data and ample amount of unlabeled data, domain-specific real-world datasets often have characteristics that both reduce the effectiveness of off-the-shelf state-of-the-art methods and also provide opportunities to create new methods that exploit these characteristics. We propose and evaluate a semi-supervised method that reuses available labels for unlabeled images of a dataset by exploiting existing similarities, while dynamically weighting the impact of these reused labels in the training process. We evaluate our method on a large dataset of human decomposition images and find that our method, while conceptually simple, outperforms state-of-the-art consistency and pseudo-labeling-based methods for the segmentation of this dataset. This paper includes graphic content of human decomposition.