GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference

• URL: http://arxiv.org/abs/2106.15064v2
• Date: Wed, 30 Jun 2021 05:25:21 GMT
• Title: GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
• Authors: Peng Tu, Yawen Huang, Rongrong Ji, Feng Zheng, Ling Shao
• Abstract summary: We propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net. We first introduce a feature alignment objective between labeled and unlabeled data to capture potentially similar image pairs. MITrans is shown to be a powerful knowledge module for further progressive refining features of unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks.
• Score: 153.354332374204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning is a challenging problem which aims to construct a model by learning from a limited number of labeled examples. Numerous methods have been proposed to tackle this problem, with most focusing on utilizing the predictions of unlabeled instances consistency alone to regularize networks. However, treating labeled and unlabeled data separately often leads to the discarding of mass prior knowledge learned from the labeled examples, and failure to mine the feature interaction between the labeled and unlabeled image pairs. In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. Specifically, we first introduce a feature alignment objective between labeled and unlabeled data to capture potentially similar image pairs and then generate mixed inputs from them. The proposed mutual information transfer (MITrans), based on the cluster assumption, is shown to be a powerful knowledge module for further progressive refining features of unlabeled data in the mixed data space. To take advantage of the labeled examples and guide unlabeled data learning, we further propose a mask generation module to generate high-quality pseudo masks for the unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, PASCAL-Context and Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive segmentation accuracy and significantly improves the mIoU by +7$\%$ compared to previous state-of-the-art approaches.

Related papers

• GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data [4.775846640214768]
  Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning. A key concept is that voxel features from labeled and unlabeled data close each other in the feature space more likely to belong to the same class. We introduce a Knowledge Transfer Cross Pseudo-label Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data.
  arXiv  Detail & Related papers  (2024-08-09T07:46:01Z)
• Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data [70.25049762295193]
  We introduce a novel conditional image generation framework that accepts noisy-labeled and uncurated data during training. We propose soft curriculum learning, which assigns instance-wise weights for adversarial training while assigning new labels for unlabeled data. Our experiments show that our approach outperforms existing semi-supervised and label-noise robust methods in terms of both quantitative and qualitative performance.
  arXiv  Detail & Related papers  (2023-07-17T08:31:59Z)
• SLaM: Student-Label Mixing for Distillation with Unlabeled Examples [15.825078347452024]
  We present a principled method for knowledge distillation with unlabeled examples that we call Student-Label Mixing (SLaM) SLaM consistently improves over prior approaches by evaluating it on several standard benchmarks. We give an algorithm improving the best-known sample complexity for learning halfspaces with margin under random classification noise.
  arXiv  Detail & Related papers  (2023-02-08T00:14:44Z)
• 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)
• Adversarial Knowledge Transfer from Unlabeled Data [62.97253639100014]
  We present a novel Adversarial Knowledge Transfer framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier. An important novel aspect of our method is that the unlabeled source data can be of different classes from those of the labeled target data, and there is no need to define a separate pretext task.
  arXiv  Detail & Related papers  (2020-08-13T08:04:27Z)
• OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World [127.64076228829606]
  We introduce OpenMix to mix the unlabeled examples from an open set and the labeled examples from known classes. OpenMix helps to prevent the model from overfitting on unlabeled samples that may be assigned with wrong pseudo-labels.
  arXiv  Detail & Related papers  (2020-04-12T05:52:39Z)
• Automatically Discovering and Learning New Visual Categories with Ranking Statistics [145.89790963544314]
  We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We learn a general-purpose clustering model and use the latter to identify the new classes in the unlabelled data. We evaluate our approach on standard classification benchmarks and outperform current methods for novel category discovery by a significant margin.
  arXiv  Detail & Related papers  (2020-02-13T18:53:32Z)

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.