SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images

• URL: http://arxiv.org/abs/2202.11900v1
• Date: Thu, 24 Feb 2022 04:58:02 GMT
• Title: SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images
• Authors: Sara Mousavi, Zhenning Yang, Kelley Cross, Dawnie Steadman, and Audris Mockus
• Abstract summary: We propose a semi-supervised method that reuses available labels for unlabeled images of a dataset by exploiting existing similarities. 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.
• Score: 5.560471251954644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey [49.47197748663787]
  This review aims to provide a first comprehensive and organized overview of the state-of-the-art research results on pseudo-label methods in the field of semi-supervised semantic segmentation. In addition, we explore the application of pseudo-label technology in medical and remote-sensing image segmentation.
  arXiv  Detail & Related papers  (2024-03-04T10:18:38Z)
• Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation [0.20305676256390934]
  Semi-supervised learning has the potential to improve the data-efficiency of training data-hungry deep neural networks. We present a simple semi-supervised learning method for lesion segmentation tasks based on the ideas of cut-paste augmentation and consistency regularization.
  arXiv  Detail & Related papers  (2022-10-01T04:43:54Z)
• Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization [98.46318529630109]
  We take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. By clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions.
  arXiv  Detail & Related papers  (2022-05-16T17:47:44Z)
• Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation [13.996217500923413]
  Semi/self-supervised learning-based approaches exploit unlabeled data along with limited annotated data. Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images. We propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information.
  arXiv  Detail & Related papers  (2021-12-17T17:38:56Z)
• Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation [11.935891325600952]
  We propose a supervised local contrastive loss that leverages limited pixel-wise annotation to force pixels with the same label to gather around in the embedding space. With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.
  arXiv  Detail & Related papers  (2021-09-15T16:23:48Z)
• Region-level Active Learning for Cluttered Scenes [60.93811392293329]
  We introduce a new strategy that subsumes previous Image-level and Object-level approaches into a generalized, Region-level approach. We show that this approach significantly decreases labeling effort and improves rare object search on realistic data with inherent class-imbalance and cluttered scenes.
  arXiv  Detail & Related papers  (2021-08-20T14:02:38Z)
• Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift [68.6874404805223]
  We propose several strategies for learning from partially overlapping labels in the context of abdominal organ segmentation. We find that combining a semi-supervised approach with an adaptive cross entropy loss can successfully exploit heterogeneously annotated data.
  arXiv  Detail & Related papers  (2021-07-13T09:22:24Z)
• Streaming Self-Training via Domain-Agnostic Unlabeled Images [62.57647373581592]
  We present streaming self-training (SST) that aims to democratize the process of learning visual recognition models. Key to SST are two crucial observations: (1) domain-agnostic unlabeled images enable us to learn better models with a few labeled examples without any additional knowledge or supervision; and (2) learning is a continuous process and can be done by constructing a schedule of learning updates.
  arXiv  Detail & Related papers  (2021-04-07T17:58:39Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Collaborative Learning of Semi-Supervised Clustering and Classification for Labeling Uncurated Data [6.871887763122593]
  Domain-specific image collections present potential value in various areas of science and business. To employ contemporary supervised image analysis methods on such image data, they must first be cleaned and organized, and then manually labeled for the nomenclature employed in the specific domain. We designed and implemented the Plud system to minimize the effort spent by an expert and handles realistic large collections of images.
  arXiv  Detail & Related papers  (2020-03-09T17:03:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.