Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

• URL: http://arxiv.org/abs/2106.01226v2
• Date: Fri, 4 Jun 2021 04:01:04 GMT
• Title: Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
• Authors: Xiaokang Chen, Yuhui Yuan, Gang Zeng, Jingdong Wang
• Abstract summary: We propose a novel consistency regularization approach, called cross pseudo supervision (CPS) The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and PASCAL VOC 2012.
• Score: 56.950950382415925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and PASCAL VOC 2012. Code is available at https://git.io/CPS.

Related papers

• Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation [16.75278876840937]
  We propose an algorithm called Multi-Level Label Correction (MLLC) to rectify erroneous pseudo-labels. MLLC can significantly improve supervised baselines and outperforms state-of-the-art approaches in different scenarios on Cityscapes and PASCAL VOC 2012 datasets.
  arXiv  Detail & Related papers  (2024-04-02T16:06:20Z)
• Unsupervised Universal Image Segmentation [59.0383635597103]
  We propose an Unsupervised Universal model (U2Seg) adept at performing various image segmentation tasks. U2Seg generates pseudo semantic labels for these segmentation tasks via leveraging self-supervised models. We then self-train the model on these pseudo semantic labels, yielding substantial performance gains.
  arXiv  Detail & Related papers  (2023-12-28T18:59:04Z)
• Pseudo-label Alignment for Semi-supervised Instance Segmentation [67.9616087910363]
  Pseudo-labeling is significant for semi-supervised instance segmentation. In existing pipelines, pseudo-labels that contain valuable information may be filtered out due to mismatches in class and mask quality. We propose a novel framework, called pseudo-label aligning instance segmentation (PAIS), in this paper.
  arXiv  Detail & Related papers  (2023-08-10T05:56:53Z)
• CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation [73.89509052503222]
  This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch. We observe that the correlation maps not only enable clustering pixels of the same category easily but also contain good shape information. We propose to conduct pixel propagation by modeling the pairwise similarities of pixels to spread the high-confidence pixels and dig out more. Then, we perform region propagation to enhance the pseudo labels with accurate class-agnostic masks extracted from the correlation maps.
  arXiv  Detail & Related papers  (2023-06-07T10:02:29Z)
• Semi-Supervised Semantic Segmentation With Region Relevance [28.92449538610617]
  Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones. The most common approach is to generate pseudo-labels for unlabeled images to augment the training data. This paper proposes a Region Relevance Network (RRN) to alleviate the problem mentioned above.
  arXiv  Detail & Related papers  (2023-04-23T04:51:27Z)
• CrossSplit: Mitigating Label Noise Memorization through Data Splitting [25.344386272010397]
  We propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit. Experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.
  arXiv  Detail & Related papers  (2022-12-03T19:09:56Z)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Deep Graph Clustering via Dual Correlation Reduction [37.973072977988494]
  We propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) In our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level. In order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information.
  arXiv  Detail & Related papers  (2021-12-29T04:05:38Z)
• GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference [90.5402652758316]
  We propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net. It uses labeled information to guide the learning of unlabeled instances. It achieves competitive segmentation accuracy and significantly improves the mIoU by +7$%$ compared to previous approaches.
  arXiv  Detail & Related papers  (2021-12-28T06:48:03Z)
• Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks [9.066817971329899]
  weakly-supervised image semantic segmentation based on image-level class labels. One common approach to this task is to propagate the activation scores of Class Activation Maps (CAMs) using a random-walk mechanism. We propose a Graph Convolutional Network (GCN)-based feature propagation framework.
  arXiv  Detail & Related papers  (2021-03-31T02:05:01Z)

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.