Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2207.10892v1
• Date: Fri, 22 Jul 2022 05:57:54 GMT
• Title: Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation
• Authors: Geon Lee, Chanho Eom, Wonkyung Lee, Hyekang Park, Bumsub Ham
• Abstract summary: A key to domain adaptive semantic segmentation is to learn domain-invariant and discriminative features without target ground-truth labels. We propose a bi-directional pixel-prototype contrastive learning framework that minimizes intra-class variations of features for the same object class.
• Score: 29.573404843110836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel unsupervised domain adaptation method for semantic segmentation that generalizes a model trained with source images and corresponding ground-truth labels to a target domain. A key to domain adaptive semantic segmentation is to learn domain-invariant and discriminative features without target ground-truth labels. To this end, we propose a bi-directional pixel-prototype contrastive learning framework that minimizes intra-class variations of features for the same object class, while maximizing inter-class variations for different ones, regardless of domains. Specifically, our framework aligns pixel-level features and a prototype of the same object class in target and source images (i.e., positive pairs), respectively, sets them apart for different classes (i.e., negative pairs), and performs the alignment and separation processes toward the other direction with pixel-level features in the source image and a prototype in the target image. The cross-domain matching encourages domain-invariant feature representations, while the bidirectional pixel-prototype correspondences aggregate features for the same object class, providing discriminative features. To establish training pairs for contrastive learning, we propose to generate dynamic pseudo labels of target images using a non-parametric label transfer, that is, pixel-prototype correspondences across different domains. We also present a calibration method compensating class-wise domain biases of prototypes gradually during training.

Related papers

• Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation [13.948425538725138]
  We propose a Pixel-Level Domain Adaptation (PLDA) method to encourage the model in learning pixel-wise domain-invariant features. We experimentally demonstrate the effectiveness of our approach under a wide range of settings.
  arXiv  Detail & Related papers  (2024-08-04T14:14:54Z)
• PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation [100.6343963798169]
  Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned model to other domains. We propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation.
  arXiv  Detail & Related papers  (2022-11-14T18:31:24Z)
• Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation [52.63046674453461]
  Prototypical Contrast Adaptation (ProCA) is a contrastive learning method for unsupervised domain adaptive semantic segmentation. ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation.
  arXiv  Detail & Related papers  (2022-07-14T04:54:26Z)
• SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation [52.62441404064957]
  Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the model trained on a labeled source domain. Many methods tend to alleviate noisy pseudo labels, however, they ignore intrinsic connections among cross-domain pixels with similar semantic concepts. We propose Semantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixel.
  arXiv  Detail & Related papers  (2022-04-19T11:16:29Z)
• SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning [6.705297811617307]
  Domain adaptation can help in transferring knowledge from a labeled source domain to an unlabeled target domain. We propose a novel semantic prototype-based contrastive learning framework for fine-grained class alignment. Our method is easy to implement and attains superior results compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-24T09:26:07Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation [50.621269117524925]
  Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. We present a semantic distribution-aware contrastive adaptation algorithm that enables pixel-wise representation alignment. We evaluate SDCA on multiple benchmarks, achieving considerable improvements over existing algorithms.
  arXiv  Detail & Related papers  (2021-05-11T13:21:25Z)
• Affinity Space Adaptation for Semantic Segmentation Across Domains [57.31113934195595]
  In this paper, we address the problem of unsupervised domain adaptation (UDA) in semantic segmentation. Motivated by the fact that source and target domain have invariant semantic structures, we propose to exploit such invariance across domains. We develop two affinity space adaptation strategies: affinity space cleaning and adversarial affinity space alignment.
  arXiv  Detail & Related papers  (2020-09-26T10:28:11Z)

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.