Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation

• URL: http://arxiv.org/abs/2110.10916v1
• Date: Thu, 21 Oct 2021 06:11:44 GMT
• Title: Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation
• Authors: Inseop Chung, Jayeon Yoo, Nojun Kwak
• Abstract summary: "Self-training" has become a dominant method for semantic segmentation via unsupervised domain adaptation (UDA) We propose a method of transferring the inter-pixel correlations from the source domain to the target domain via a self-attention module.
• Score: 32.71144601174794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: "Self-training" has become a dominant method for semantic segmentation via unsupervised domain adaptation (UDA). It creates a set of pseudo labels for the target domain to give explicit supervision. However, the pseudo labels are noisy, sparse and do not provide any information about inter-pixel correlations. We regard inter-pixel correlation quite important because semantic segmentation is a task of predicting highly structured pixel-level outputs. Therefore, in this paper, we propose a method of transferring the inter-pixel correlations from the source domain to the target domain via a self-attention module. The module takes the prediction of the segmentation network as an input and creates a self-attended prediction that correlates similar pixels. The module is trained only on the source domain to learn the domain-invariant inter-pixel correlations, then later, it is used to train the segmentation network on the target domain. The network learns not only from the pseudo labels but also by following the output of the self-attention module which provides additional knowledge about the inter-pixel correlations. Through extensive experiments, we show that our method significantly improves the performance on two standard UDA benchmarks and also can be combined with recent state-of-the-art method to achieve better performance.

Related papers

• SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote Sensing [14.007392647145448]
  UDA enables models to learn from unlabeled target domain data while training on labeled source domain data. We propose integrating contrastive learning into UDA, enhancing the model's capacity to capture semantic information. Our SimSeg method outperforms existing approaches, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-17T11:59:39Z)
• PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning [34.786268652516355]
  Unsupervised domain adaptive segmentation aims to improve the segmentation accuracy of models on target domains without relying on labeled data from those domains. It seeks to align the feature representations of the source domain (where labeled data is available) and the target domain (where only unlabeled data is present)
  arXiv  Detail & Related papers  (2024-07-24T08:53:29Z)
• 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)
• 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)
• DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [78.30720731968135]
  Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. We propose DecoupleNet that alleviates source domain overfitting and enables the final model to focus more on the segmentation task. We also put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels.
  arXiv  Detail & Related papers  (2022-07-20T15:47:34Z)
• 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)
• 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)
• More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation [41.81843755299211]
  We propose a new UDA semantic segmentation approach based on domain assumption closeness to alleviate the above problems. Specifically, a prototype clustering strategy is applied to cluster pixels with the same semantic, which will better maintain associations among target domain pixels. Experiments conducted on GTA5 and SYNTHIA proved the effectiveness of our method.
  arXiv  Detail & Related papers  (2021-05-07T10:24:18Z)
• Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation [169.82760468633236]
  We propose to build the pixel-level cycle association between source and target pixel pairs. Our method can be trained end-to-end in one stage and introduces no additional parameters.
  arXiv  Detail & Related papers  (2020-10-31T00:11:36Z)
• 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.