COARSE: Collaborative Pseudo-Labeling with Coarse Real Labels for Off-Road Semantic Segmentation

• URL: http://arxiv.org/abs/2503.03947v1
• Date: Wed, 05 Mar 2025 22:25:54 GMT
• Title: COARSE: Collaborative Pseudo-Labeling with Coarse Real Labels for Off-Road Semantic Segmentation
• Authors: Aurelio Noca, Xianmei Lei, Jonathan Becktor, Jeffrey Edlund, Anna Sabel, Patrick Spieler, Curtis Padgett, Alexandre Alahi, Deegan Atha,
• Abstract summary: COARSE is a semi-supervised domain adaptation framework for off-road semantic segmentation.<n>We bridge domain gaps with complementary pixel-level and patch-level decoders, enhanced by a collaborative pseudo-labeling strategy.
• Score: 49.267650162344765
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Autonomous off-road navigation faces challenges due to diverse, unstructured environments, requiring robust perception with both geometric and semantic understanding. However, scarce densely labeled semantic data limits generalization across domains. Simulated data helps, but introduces domain adaptation issues. We propose COARSE, a semi-supervised domain adaptation framework for off-road semantic segmentation, leveraging sparse, coarse in-domain labels and densely labeled out-of-domain data. Using pretrained vision transformers, we bridge domain gaps with complementary pixel-level and patch-level decoders, enhanced by a collaborative pseudo-labeling strategy on unlabeled data. Evaluations on RUGD and Rellis-3D datasets show significant improvements of 9.7\% and 8.4\% respectively, versus only using coarse data. Tests on real-world off-road vehicle data in a multi-biome setting further demonstrate COARSE's applicability.

Related papers

• AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation [10.958821619282748]
  We present an unsupervised domain adaptation (UDA) method named AdaptDiff. It enables a retinal vessel segmentation network trained on fundus photography (FP) to produce satisfactory results on unseen modalities. Our results demonstrate a significant improvement in segmentation performance across all unseen datasets.
  arXiv  Detail & Related papers  (2024-10-06T23:04:29Z)
• Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation [1.5393913074555419]
  We introduce SegCLR, a versatile framework designed to segment images across different domains.<n>SegCLR employs supervised and contrastive learning simultaneously to effectively learn from both labeled and unlabeled data.<n>We demonstrate the superior performance of SegCLR through a comprehensive evaluation involving three diverse clinical datasets.
  arXiv  Detail & Related papers  (2024-05-08T18:10:59Z)
• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation [108.40945109477886]
  We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment. Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
  arXiv  Detail & Related papers  (2024-01-21T09:57:56Z)
• QuadFormer: Quadruple Transformer for Unsupervised Domain Adaptation in Power Line Segmentation of Aerial Images [12.840195641761323]
  We propose a novel framework designed for domain adaptive semantic segmentation. The hierarchical quadruple transformer combines cross-attention and self-attention mechanisms to adapt transferable context. We present two datasets - ARPLSyn and ARPLReal - to further advance research in unsupervised domain adaptive powerline segmentation.
  arXiv  Detail & Related papers  (2022-11-29T03:15:27Z)
• Unsupervised Contrastive Domain Adaptation for Semantic Segmentation [75.37470873764855]
  We introduce contrastive learning for feature alignment in cross-domain adaptation. The proposed approach consistently outperforms state-of-the-art methods for domain adaptation. It achieves 60.2% mIoU on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2022-04-18T16:50:46Z)
• 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)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation [71.77083272602525]
  UDA attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain. We propose a contrastive learning approach that adapts category-wise centroids across domains. We extend our method with self-training, where we use a memory-efficient temporal ensemble to generate consistent and reliable pseudo-labels.
  arXiv  Detail & Related papers  (2021-05-05T11:55:53Z)
• DACS: Domain Adaptation via Cross-domain Mixed Sampling [4.205692673448206]
  Unsupervised domain adaptation attempts to train on labelled data from one domain, and simultaneously learn from unlabelled data in the domain of interest. We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo-labels. We demonstrate the effectiveness of our solution by achieving state-of-the-art results for GTA5 to Cityscapes.
  arXiv  Detail & Related papers  (2020-07-17T00:43:11Z)
• Synthetic-to-Real Domain Adaptation for Lane Detection [5.811502603310248]
  We explore learning from abundant, randomly generated synthetic data, together with unlabeled or partially labeled target domain data. This poses the challenge of adapting models learned on the unrealistic synthetic domain to real images. We develop a novel autoencoder-based approach that uses synthetic labels unaligned with particular images for adapting to target domain data.
  arXiv  Detail & Related papers  (2020-07-08T10:54:21Z)

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.