SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2204.08808v1
• Date: Tue, 19 Apr 2022 11:16:29 GMT
• Title: SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
• Authors: Binhui Xie, Shuang Li, Mingjia Li, Chi Harold Liu, Gao Huang and Guoren Wang
• Abstract summary: 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.
• Score: 52.62441404064957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. One solution is self-training, which retrains models with target pseudo labels. Many methods tend to alleviate noisy pseudo labels, however, they ignore intrinsic connections among cross-domain pixels with similar semantic concepts. Thus, they would struggle to deal with the semantic variations across domains, leading to less discrimination and poor generalization. In this work, we propose Semantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixel to promote learning of class-discriminative and class-balanced pixel embedding space across domains. Specifically, to explore proper semantic concepts, we first investigate a centroid-aware pixel contrast that employs the category centroids of the entire source domain or a single source image to guide the learning of discriminative features. Considering the possible lack of category diversity in semantic concepts, we then blaze a trail of distributional perspective to involve a sufficient quantity of instances, namely distribution-aware pixel contrast, in which we approximate the true distribution of each semantic category from the statistics of labeled source data. Moreover, such an optimization objective can derive a closed-form upper bound by implicitly involving an infinite number of (dis)similar pairs. Extensive experiments show that SePiCo not only helps stabilize training but also yields discriminative features, making significant progress in both daytime and nighttime scenarios. Most notably, SePiCo establishes excellent results on tasks of GTAV/SYNTHIA-to-Cityscapes and Cityscapes-to-Dark Zurich, improving by 12.8, 8.8, and 9.2 mIoUs compared to the previous best method, respectively.

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)
• 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)
• 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)
• Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation [16.950853152484203]
  We consider the task of semantic segmentation in autonomous driving applications. In this context, aligning the domains is made more challenging by the pixel-wise class imbalance. We propose a novel framework called Pixel-By-Pixel Cross-Domain Alignment (PixDA)
  arXiv  Detail & Related papers  (2021-10-22T08:27:17Z)
• Domain Adaptive Semantic Segmentation without Source Data [50.18389578589789]
  We investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain. We propose an effective framework for this challenging problem with two components: positive learning and negative learning. Our framework can be easily implemented and incorporated with other methods to further enhance the performance.
  arXiv  Detail & Related papers  (2021-10-13T04:12:27Z)
• 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)
• 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.