Active Label Correction for Semantic Segmentation with Foundation Models

• URL: http://arxiv.org/abs/2403.10820v2
• Date: Tue, 4 Jun 2024 13:15:16 GMT
• Title: Active Label Correction for Semantic Segmentation with Foundation Models
• Authors: Hoyoung Kim, Sehyun Hwang, Suha Kwak, Jungseul Ok,
• Abstract summary: We propose an effective framework of active label correction (ALC) based on a design of correction query to rectify pseudo labels of pixels. Our method comprises two key techniques: (i) an annotator-friendly design of correction query with the pseudo labels, and (ii) an acquisition function looking ahead label expansions based on the superpixels. Experimental results on PASCAL, Cityscapes, and Kvasir-SEG datasets demonstrate the effectiveness of our ALC framework.
• Score: 34.0733215363568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundation models or crowdsourced datasets are available, they are error-prone. We hence propose an effective framework of active label correction (ALC) based on a design of correction query to rectify pseudo labels of pixels, which in turn is more annotator-friendly than the standard one inquiring to classify a pixel directly according to our theoretical analysis and user study. Specifically, leveraging foundation models providing useful zero-shot predictions on pseudo labels and superpixels, our method comprises two key techniques: (i) an annotator-friendly design of correction query with the pseudo labels, and (ii) an acquisition function looking ahead label expansions based on the superpixels. Experimental results on PASCAL, Cityscapes, and Kvasir-SEG datasets demonstrate the effectiveness of our ALC framework, outperforming prior methods for active semantic segmentation and label correction. Notably, utilizing our method, we obtained a revised dataset of PASCAL by rectifying errors in 2.6 million pixels in PASCAL dataset.

Related papers

• ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction [57.930531826380836]
  This work explores whether a foundational segmentation model can address label scarcity in the pixel-level vision task as an annotator for unlabeled images.<n>We propose ConformalSAM, a novel SSSS framework which first calibrates the foundation model using the target domain's labeled data and then filters out unreliable pixel labels of unlabeled data.
  arXiv  Detail & Related papers  (2025-07-21T17:02:57Z)
• Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation [0.0]
  We propose a method to train a semantic segmentation model using images with annotated labels and pseudo labels.<n>The accuracy of the model depends on the quality of the pseudo labels and the amount of data with annotated labels.<n>The effectiveness of the proposed method is demonstrated through the experiments using the public datasets: PASCAL and MS COCO.
  arXiv  Detail & Related papers  (2025-05-26T11:31:13Z)
• Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation [62.55963720723179]
  Semi-Supervised Instance (SSIS) involves classifying and grouping image pixels into distinct object instances.<n>This learning paradigm usually faces a significant challenge of unstable performance caused by noisy pseudo-labels of instance categories and pixel masks.<n>We introduce a novel PseudoLabel Quality Decoupling and Correction (PL-DC) framework for tackling the above challenges.
  arXiv  Detail & Related papers  (2025-05-16T10:07:17Z)
• Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation [52.239136918460616]
  Extending binary class tags to approximate relative object-size distributions allows off-the-shelf architectures to solve the segmentation problem. A straightforward zero-avoiding KL-divergence loss for average predictions produces segmentation accuracy comparable to the standard pixel-precise supervision. Our ideas are validated on PASCAL VOC using our new human annotations of approximate object sizes.
  arXiv  Detail & Related papers  (2025-03-10T06:02:13Z)
• HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation [47.271784693700845]
  We propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels. Our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset.
  arXiv  Detail & Related papers  (2024-03-25T14:02:33Z)
• Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation [89.41179071022121]
  Self-training is a prevailing approach in cross-domain semantic segmentation. We propose a novel approach called Semantic Connectivity-driven pseudo-labeling. This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
  arXiv  Detail & Related papers  (2023-12-11T12:29:51Z)
• Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation [25.628382644404066]
  We introduce a novel ViT-based WSSS method named top-K pooling with patch contrastive learning (TKP-PCL) A patch contrastive error (PCE) is also proposed to enhance the patch embeddings to further improve the final results. Our approach is very efficient and outperforms other state-of-the-art WSSS methods on the PASCAL 2012 dataset.
  arXiv  Detail & Related papers  (2023-10-15T13:19:59Z)
• Estimating label quality and errors in semantic segmentation data via any model [19.84626033109009]
  We study methods to score label quality, such that the images with the lowest scores are least likely to be correctly labeled. This helps prioritize what data to review in order to ensure a high-quality training/evaluation dataset.
  arXiv  Detail & Related papers  (2023-07-11T07:29:09Z)
• Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation [58.03255076119459]
  We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions. Experiments on Pascal-5i and COCO-20i demonstrate significant performance gains in a variety of supervision settings.
  arXiv  Detail & Related papers  (2023-07-07T06:16:43Z)
• 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)
• CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation [5.484296906525601]
  Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. We propose a class-adaptive semisupervision framework for semi-supervised semantic segmentation (CAFS) CAFS constructs a validation set on a labeled dataset, to leverage the calibration performance for each class.
  arXiv  Detail & Related papers  (2023-03-21T05:56:53Z)
• Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks [68.36996813591425]
  We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks. We enable the application of FixMatch in semi-supervised learning problems beyond image classification by adding a matching operation on the pseudo-labels. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.
  arXiv  Detail & Related papers  (2022-10-18T15:02:51Z)
• Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition [98.25592165484737]
  We propose a more effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL) CMPL achieves $17.6%$ and $25.1%$ Top-1 accuracy on Kinetics-400 and UCF-101 using only the RGB modality and $1%$ labeled data, respectively.
  arXiv  Detail & Related papers  (2021-12-17T18:59:41Z)
• LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation [79.96052264984469]
  We propose a human-in-the-loop approach to adaptively give scarce labels to points that a UDA model is uncertain about. We show the advantages of this new framework for domain adaptive semantic segmentation while minimizing human labor costs.
  arXiv  Detail & Related papers  (2021-08-12T07:35:40Z)

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.