Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation

• URL: http://arxiv.org/abs/2101.07253v1
• Date: Mon, 18 Jan 2021 18:59:21 GMT
• Title: Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
• Authors: Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, \'Emilie Wirbel, and Patrick P\'erez
• Abstract summary: Domain adaptation is an important task to enable learning when labels are scarce. We propose cross-modal learning, where we enforce consistency between the predictions of two modalities via mutual mimicking. We constrain our network to make correct predictions on labeled data and consistent predictions across modalities on unlabeled target-domain data.
• Score: 11.895722159139108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptation is an important task to enable learning when labels are scarce. While most works focus only on the image modality, there are many important multi-modal datasets. In order to leverage multi-modality for domain adaptation, we propose cross-modal learning, where we enforce consistency between the predictions of two modalities via mutual mimicking. We constrain our network to make correct predictions on labeled data and consistent predictions across modalities on unlabeled target-domain data. Experiments in unsupervised and semi-supervised domain adaptation settings prove the effectiveness of this novel domain adaptation strategy. Specifically, we evaluate on the task of 3D semantic segmentation using the image and point cloud modality. We leverage recent autonomous driving datasets to produce a wide variety of domain adaptation scenarios including changes in scene layout, lighting, sensor setup and weather, as well as the synthetic-to-real setup. Our method significantly improves over previous uni-modal adaptation baselines on all adaption scenarios. Code will be made available.

Related papers

• 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)
• Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation [65.78246406460305]
  compositional semantic mixing represents the first unsupervised domain adaptation technique for point cloud segmentation. We present a two-branch symmetric network architecture capable of concurrently processing point clouds from a source domain (e.g. synthetic) and point clouds from a target domain (e.g. real-world)
  arXiv  Detail & Related papers  (2023-08-28T14:43:36Z)
• Domain Adaptive Semantic Segmentation by Optimal Transport [13.133890240271308]
  semantic scene segmentation has received a great deal of attention due to the richness of the semantic information it contains. Current approaches are mainly based on convolutional neural networks (CNN), but they rely on a large number of labels. We propose a domain adaptation (DA) framework based on optimal transport (OT) and attention mechanism to address this issue.
  arXiv  Detail & Related papers  (2023-03-29T03:33:54Z)
• An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions [5.217255784808035]
  We propose an unsupervised domain adaptation framework to bridge the domain gap between source and target domains. We use a data augmentation scheme that simulates weather distortions to add domain confusion and prevent overfitting on the source data. Experiments performed on the DENSE dataset show that our method can substantially alleviate the domain gap.
  arXiv  Detail & Related papers  (2022-03-07T18:10:40Z)
• 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)
• Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation [32.95273803359897]
  Multi-source unsupervised domain adaptation(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. We propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation.
  arXiv  Detail & Related papers  (2021-03-08T12:51:42Z)
• Domain Adaptation for Semantic Parsing [68.81787666086554]
  We propose a novel semantic for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies.
  arXiv  Detail & Related papers  (2020-06-23T14:47:41Z)
• Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training [22.366638308792734]
  Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled data is available. We propose an approach to adapt a deep neural network trained on synthetic data to real scenes addressing the domain shift between the two different data distributions.
  arXiv  Detail & Related papers  (2020-04-27T11:48:03Z)
• Phase Consistent Ecological Domain Adaptation [76.75730500201536]
  We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
  arXiv  Detail & Related papers  (2020-04-10T06:58:03Z)
• Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
  Unsupervised domain adaptation is critical in various computer vision tasks. We design a new spatial attention pyramid network for unsupervised domain adaptation. Our method performs favorably against the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-03-29T09:03:23Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.