Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey

• URL: http://arxiv.org/abs/2403.01909v1
• Date: Mon, 4 Mar 2024 10:18:38 GMT
• Title: Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
• Authors: Lingyan Ran, Yali Li, Guoqiang Liang, and Yanning Zhang
• Abstract summary: 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.
• Score: 49.47197748663787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. However, supervised deep learning requires large amounts of data to train models and the process of labeling images pixel by pixel is time-consuming and laborious. 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, which we categorize from different perspectives and present specific methods for specific application areas. In addition, we explore the application of pseudo-label technology in medical and remote-sensing image segmentation. Finally, we also propose some feasible future research directions to address the existing challenges.

Related papers

• Image Segmentation in Foundation Model Era: A Survey [99.19456390358211]
  Current research in image segmentation lacks a detailed analysis of distinct characteristics, challenges, and solutions associated with these advancements. This survey seeks to fill this gap by providing a thorough review of cutting-edge research centered around FM-driven image segmentation. An exhaustive overview of over 300 segmentation approaches is provided to encapsulate the breadth of current research efforts.
  arXiv  Detail & Related papers  (2024-08-23T10:07:59Z)
• UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks [9.268228808049951]
  This research contributes to the broader field of unsupervised medical imaging and computer vision. It presents an innovative methodology for image segmentation that aligns with real-world challenges. The proposed method holds promise for diverse applications, including medical imaging, remote sensing, and object recognition.
  arXiv  Detail & Related papers  (2024-05-09T19:02:00Z)
• Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models [33.690846523358836]
  Weakly-supervised semantic segmentation (WSSS) is an effective solution to avoid pixel-level labels. We focus on the WSSS with image-level labels, which is the most challenging form of WSSS. We investigate the applicability of visual foundation models, such as the Segment Anything Model (SAM), in the context of WSSS.
  arXiv  Detail & Related papers  (2023-10-19T07:16:54Z)
• Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding [95.78002228538841]
  We propose a new open-world semantic segmentation pipeline that makes the first attempt to learn to segment semantic objects of various open-world categories without any efforts on dense annotations. Our method can directly segment objects of arbitrary categories, outperforming zero-shot segmentation methods that require data labeling on three benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-18T09:20:04Z)
• A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation [40.27705176115985]
  Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. We propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. Our proposed learning model can be viewed as a pixel-level meta-learner.
  arXiv  Detail & Related papers  (2021-11-02T08:28:11Z)
• Segmentation of VHR EO Images using Unsupervised Learning [19.00071868539993]
  We propose an unsupervised semantic segmentation method that can be trained using just a single unlabeled scene. The proposed method exploits this property to sample smaller patches from the larger scene. After unsupervised training on the target image/scene, the model automatically segregates the major classes present in the scene and produces the segmentation map.
  arXiv  Detail & Related papers  (2021-07-09T11:42:48Z)
• Hierarchical Semantic Segmentation using Psychometric Learning [17.417302703539367]
  We develop a novel approach to collect segmentation annotations from experts based on psychometric testing. Our method consists of the psychometric testing procedure, active query selection, query enhancement, and a deep metric learning model. We show the merits of our method with evaluation on the synthetically generated image, aerial image and histology image.
  arXiv  Detail & Related papers  (2021-07-07T13:38:33Z)
• Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter [62.26677215668959]
  We propose a lightweight, weakly supervised deep network to coarsely locate semantically salient regions. We then fuse multiple off-the-shelf deep models on these semantically salient regions as the pixel-wise saliency refinement. Our method is simple yet effective, which is the first attempt to consider the salient object detection mainly as an object-level semantic re-ranking problem.
  arXiv  Detail & Related papers  (2020-08-10T07:12:43Z)
• Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping [71.91734471596432]
  We propose SEG-GRAD-CAM, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.
  arXiv  Detail & Related papers  (2020-02-26T12:32:40Z)
• Image Segmentation Using Deep Learning: A Survey [58.37211170954998]
  Image segmentation is a key topic in image processing and computer vision. There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
  arXiv  Detail & Related papers  (2020-01-15T21:37:47Z)

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.