BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation

• URL: http://arxiv.org/abs/2308.16819v3
• Date: Thu, 12 Sep 2024 07:34:45 GMT
• Title: BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation
• Authors: Johannes Künzel, Anna Hilsmann, Peter Eisert,
• Abstract summary: We introduce BTSeg, an innovative, semi-supervised training approach enhancing semantic segmentation models. Images captured at similar locations but under varying adverse conditions are regarded as manifold representation of the same scene, thereby enabling the model to conceptualize its understanding of the environment.
• Score: 3.5229503563299915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce BTSeg (Barlow Twins regularized Segmentation), an innovative, semi-supervised training approach enhancing semantic segmentation models in order to effectively tackle adverse weather conditions without requiring additional labeled training data. Images captured at similar locations but under varying adverse conditions are regarded as manifold representation of the same scene, thereby enabling the model to conceptualize its understanding of the environment. BTSeg shows cutting-edge performance for the new challenging ACG benchmark and sets a new state-of-the-art for weakly-supervised domain adaptation for the ACDC dataset. To support further research, we have made our code publicly available at https://github.com/fraunhoferhhi/BTSeg .

Related papers

• No time to train! Training-Free Reference-Based Instance Segmentation [15.061599989448867]
  This work investigates the task of object segmentation when provided with only a small set of reference images.<n>Our key insight is to leverage strong semantic priors, as learned by foundation models, to identify corresponding regions between a reference and a target image.<n>We find that correspondences enable automatic generation of instance-level segmentation masks for downstream tasks and instantiate our ideas via a multi-stage, training-free method.
  arXiv  Detail & Related papers  (2025-07-03T16:59:01Z)
• Physically Feasible Semantic Segmentation [58.17907376475596]
  State-of-the-art semantic segmentation models are typically optimized in a data-driven fashion. Our method, Physically Feasible Semantic (PhyFea), extracts explicit physical constraints that govern spatial class relations. PhyFea yields significant performance improvements in mIoU over each state-of-the-art network we use.
  arXiv  Detail & Related papers  (2024-08-26T22:39:08Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Condition-Invariant Semantic Segmentation [77.10045325743644]
  We implement Condition-Invariant Semantic (CISS) on the current state-of-the-art domain adaptation architecture. Our method achieves the second-best performance on the normal-to-adverse Cityscapes$to$ACDC benchmark. CISS is shown to generalize well to domains unseen during training, such as BDD100K-night and ACDC-night.
  arXiv  Detail & Related papers  (2023-05-27T03:05:07Z)
• FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding [27.05038930059941]
  Domain Adaptation in Semantic Scene has shown impressive improvement in recent years. Fairness is one of the most critical aspects when deploying the segmentation models into human-related real-world applications. In this paper, we propose a novel Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation.
  arXiv  Detail & Related papers  (2023-04-04T21:35:10Z)
• Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation [58.17907376475596]
  We investigate normal-to-adverse condition model adaptation for semantic segmentation. Our method -- CMA -- leverages such image pairs to learn condition-invariant features via contrastive learning. We achieve state-of-the-art semantic segmentation performance for model adaptation on several normal-to-adverse adaptation benchmarks.
  arXiv  Detail & Related papers  (2023-03-09T11:48:29Z)
• Consistency Regularization for Domain Adaptation [17.067348505083327]
  Unsupervised domain adaptation (UDA) tries to solve this problem by studying how more accessible data can be used to train and adapt models to real world images. Recent UDA methods applies self-learning by training on pixel-wise classification loss using a student and teacher network. We propose the addition of a consistency regularization term to semi-supervised UDA by modelling the inter-pixel relationship between elements in networks' output.
  arXiv  Detail & Related papers  (2022-08-23T17:07:06Z)
• Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions [78.71745819446176]
  Refign is a generic extension to self-training-based UDA methods which leverages cross-domain correspondences. Refign consists of two steps: (1) aligning the normal-condition image to the corresponding adverse-condition image using an uncertainty-aware dense matching network, and (2) refining the adverse prediction with the normal prediction using an adaptive label correction mechanism. The approach introduces no extra training parameters, minimal computational overhead -- during training only -- and can be used as a drop-in extension to improve any given self-training-based UDA method.
  arXiv  Detail & Related papers  (2022-07-14T11:30:38Z)
• Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation [30.104947024614127]
  We formalize the task of unsupervised domain adaptation for panoramic semantic segmentation. DensePASS is a novel dataset for panoramic segmentation under cross-domain conditions. We introduce P2PDA - a generic framework for Pinhole-to-Panoramic semantic segmentation.
  arXiv  Detail & Related papers  (2021-10-21T11:22:05Z)
• Self-supervised Augmentation Consistency for Adapting Semantic Segmentation [56.91850268635183]
  We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. We employ standard data augmentation techniques $-$ photometric noise, flipping and scaling $-$ and ensure consistency of the semantic predictions. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios.
  arXiv  Detail & Related papers  (2021-04-30T21:32:40Z)
• Transferring and Regularizing Prediction for Semantic Segmentation [115.88957139226966]
  In this paper, we exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer. We present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties as constraints to regularize model transfer in an unsupervised fashion. Extensive experiments are conducted to verify the proposal of RPT on the transfer of models trained on GTA5 and SYNTHIA (synthetic data) to Cityscapes dataset (urban street scenes)
  arXiv  Detail & Related papers  (2020-06-11T16:19:41Z)

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.