SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation

• URL: http://arxiv.org/abs/2407.12788v1
• Date: Mon, 17 Jun 2024 13:40:42 GMT
• Title: SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation
• Authors: Weihao Yan, Yeqiang Qian, Yueyuan Li, Tao Li, Chunxiang Wang, Ming Yang,
• Abstract summary: We propose a novel semi-supervised active domain adaptation (SS-ADA) framework for semantic segmentation. SS-ADA integrates active learning into semi-supervised semantic segmentation to achieve the accuracy of supervised learning. We conducted extensive experiments on synthetic-to-real and real-to-real domain adaptation settings.
• Score: 25.929173344653158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation plays an important role in intelligent vehicles, providing pixel-level semantic information about the environment. However, the labeling budget is expensive and time-consuming when semantic segmentation model is applied to new driving scenarios. To reduce the costs, semi-supervised semantic segmentation methods have been proposed to leverage large quantities of unlabeled images. Despite this, their performance still falls short of the accuracy required for practical applications, which is typically achieved by supervised learning. A significant shortcoming is that they typically select unlabeled images for annotation randomly, neglecting the assessment of sample value for model training. In this paper, we propose a novel semi-supervised active domain adaptation (SS-ADA) framework for semantic segmentation that employs an image-level acquisition strategy. SS-ADA integrates active learning into semi-supervised semantic segmentation to achieve the accuracy of supervised learning with a limited amount of labeled data from the target domain. Additionally, we design an IoU-based class weighting strategy to alleviate the class imbalance problem using annotations from active learning. We conducted extensive experiments on synthetic-to-real and real-to-real domain adaptation settings. The results demonstrate the effectiveness of our method. SS-ADA can achieve or even surpass the accuracy of its supervised learning counterpart with only 25% of the target labeled data when using a real-time segmentation model. The code for SS-ADA is available at https://github.com/ywher/SS-ADA.

Related papers

• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation [40.803251337200656]
  Annotator is a general and efficient active learning baseline. voxel-centric online selection strategy is tailored to efficiently probe and annotate the salient and exemplar voxel girds within each LiDAR scan. Annotator excels in diverse settings, with a particular focus on active learning (AL), active source-free domain adaptation (ASFDA), and active domain adaptation (ADA)
  arXiv  Detail & Related papers  (2023-10-31T09:04:39Z)
• Divide and Adapt: Active Domain Adaptation via Customized Learning [56.79144758380419]
  We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties. With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples. Thanks to the "divideand-adapt" spirit, DiaNA can handle data with large variations of domain gap.
  arXiv  Detail & Related papers  (2023-07-21T14:37:17Z)
• Revisiting Deep Active Learning for Semantic Segmentation [37.3546941940388]
  We show that the data distribution is decisive for the performance of the various active learning objectives proposed in the literature. We demonstrate that the integration of semi-supervised learning with active learning can improve performance when the two objectives are aligned.
  arXiv  Detail & Related papers  (2023-02-08T14:23:37Z)
• TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation [7.856339385917824]
  This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation. Our results on a publicly-available dataset of cardiac images show that TAAL outperforms existing baseline methods in both fully-supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2023-01-16T22:19:41Z)
• An Efficient Active Learning Pipeline for Legal Text Classification [2.462514989381979]
  We propose a pipeline for effectively using active learning with pre-trained language models in the legal domain. We use knowledge distillation to guide the model's embeddings to a semantically meaningful space. Our experiments on Contract-NLI, adapted to the classification task, and LEDGAR benchmarks show that our approach outperforms standard AL strategies.
  arXiv  Detail & Related papers  (2022-11-15T13:07:02Z)
• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• Active Pointly-Supervised Instance Segmentation [106.38955769817747]
  We present an economic active learning setting, named active pointly-supervised instance segmentation (APIS) APIS starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset.
  arXiv  Detail & Related papers  (2022-07-23T11:25:24Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Semi-Supervised Domain Adaptation with Prototypical Alignment and Consistency Learning [86.6929930921905]
  This paper studies how much it can help address domain shifts if we further have a few target samples labeled. To explore the full potential of landmarks, we incorporate a prototypical alignment (PA) module which calculates a target prototype for each class from the landmarks. Specifically, we severely perturb the labeled images, making PA non-trivial to achieve and thus promoting model generalizability.
  arXiv  Detail & Related papers  (2021-04-19T08:46:08Z)

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.