A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2012.00827v1
• Date: Tue, 1 Dec 2020 21:00:27 GMT
• Title: A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
• Authors: Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey, Saikumar Reddy and Carola-Bibiane Sch\"onlieb
• Abstract summary: We propose a holistic solution framed as a three-stage self-training framework for semantic segmentation. The key idea of our technique is the extraction of the pseudo-masks statistical information. We then decrease the uncertainty of the pseudo-masks using a multi-task model that enforces consistency.
• Score: 0.9786690381850356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation has been widely investigated in the community, in which the state of the art techniques are based on supervised models. Those models have reported unprecedented performance at the cost of requiring a large set of high quality segmentation masks. To obtain such annotations is highly expensive and time consuming, in particular, in semantic segmentation where pixel-level annotations are required. In this work, we address this problem by proposing a holistic solution framed as a three-stage self-training framework for semi-supervised semantic segmentation. The key idea of our technique is the extraction of the pseudo-masks statistical information to decrease uncertainty in the predicted probability whilst enforcing segmentation consistency in a multi-task fashion. We achieve this through a three-stage solution. Firstly, we train a segmentation network to produce rough pseudo-masks which predicted probability is highly uncertain. Secondly, we then decrease the uncertainty of the pseudo-masks using a multi-task model that enforces consistency whilst exploiting the rich statistical information of the data. We compare our approach with existing methods for semi-supervised semantic segmentation and demonstrate its state-of-the-art performance with extensive experiments.

Related papers

• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Learning to Generate Training Datasets for Robust Semantic Segmentation [37.9308918593436]
  We propose a novel approach to improve the robustness of semantic segmentation techniques. We design Robusta, a novel conditional generative adversarial network to generate realistic and plausible perturbed images. Our results suggest that this approach could be valuable in safety-critical applications.
  arXiv  Detail & Related papers  (2023-08-01T10:02:26Z)
• Denoising Diffusion Semantic Segmentation with Mask Prior Modeling [61.73352242029671]
  We propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a denoising diffusion generative model. We evaluate the proposed prior modeling with several off-the-shelf segmentors, and our experimental results on ADE20K and Cityscapes demonstrate that our approach could achieve competitively quantitative performance.
  arXiv  Detail & Related papers  (2023-06-02T17:47:01Z)
• Masked Supervised Learning for Semantic Segmentation [5.177947445379688]
  Masked Supervised Learning (MaskSup) is an effective single-stage learning paradigm that models both short- and long-range context. We show that the proposed method is computationally efficient, yielding an improved performance by 10% on the mean intersection-over-union (mIoU)
  arXiv  Detail & Related papers  (2022-10-03T13:30:19Z)
• Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation [48.294903659573585]
  In this paper, we propose to embed affinity learning of multi-stage approaches in a single-stage model. A deep neural network is used to deliver comprehensive semantic information in the training phase. Experiments are conducted on the PASCAL VOC 2012 dataset to evaluate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2021-08-03T07:48:33Z)
• Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation [90.87105131054419]
  We present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains.
  arXiv  Detail & Related papers  (2020-12-19T21:18:03Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)
• ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning [4.205692673448206]
  We propose a novel data augmentation mechanism called ClassMix, which generates augmentations by mixing unlabelled samples. We evaluate this augmentation technique on two common semi-supervised semantic segmentation benchmarks, showing that it attains state-of-the-art results.
  arXiv  Detail & Related papers  (2020-07-15T18:21:17Z)

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.