IDA: Informed Domain Adaptive Semantic Segmentation
- URL: http://arxiv.org/abs/2303.02741v1
- Date: Sun, 5 Mar 2023 18:16:34 GMT
- Title: IDA: Informed Domain Adaptive Semantic Segmentation
- Authors: Zheng Chen, Zhengming Ding, Jason M. Gregory, and Lantao Liu
- Abstract summary: We propose an Domain Informed Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance.
In our IDA model, the class-level performance is tracked by an expected confidence score (ECS) and we then use a dynamic schedule to determine the mixing ratio for data in different domains.
Our proposed method is able to outperform the state-of-the-art UDA-SS method by a margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in the adaptation of SYNTHIA to City
- Score: 51.12107564372869
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mixup-based data augmentation has been validated to be a critical stage in
the self-training framework for unsupervised domain adaptive semantic
segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated
(source) domain to an unlabeled (target) domain. Existing self-training methods
usually adopt the popular region-based mixup techniques with a random sampling
strategy, which unfortunately ignores the dynamic evolution of different
semantics across various domains as training proceeds. To improve the UDA-SS
performance, we propose an Informed Domain Adaptation (IDA) model, a
self-training framework that mixes the data based on class-level segmentation
performance, which aims to emphasize small-region semantics during mixup. In
our IDA model, the class-level performance is tracked by an expected confidence
score (ECS). We then use a dynamic schedule to determine the mixing ratio for
data in different domains. Extensive experimental results reveal that our
proposed method is able to outperform the state-of-the-art UDA-SS method by a
margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in
the adaptation of SYNTHIA to Cityscapes.
Related papers
- Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition [1.2878987353423252]
Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR)
We introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process.
We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples.
arXiv Detail & Related papers (2024-10-13T16:40:48Z) - Style Adaptation for Domain-adaptive Semantic Segmentation [2.1365683052370046]
Domain discrepancy leads to a significant decrease in the performance of general network models trained on the source domain data when applied to the target domain.
We introduce a straightforward approach to mitigate the domain discrepancy, which necessitates no additional parameter calculations and seamlessly integrates with self-training-based UDA methods.
Our proposed method attains a noteworthy UDA performance of 76.93 mIoU on the GTA->Cityscapes dataset, representing a notable improvement of +1.03 percentage points over the previous state-of-the-art results.
arXiv Detail & Related papers (2024-04-25T02:51:55Z) - Divide and Adapt: Active Domain Adaptation via Customized Learning [56.79144758380419]
We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties.
With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples.
Thanks to the "divideand-adapt" spirit, DiaNA can handle data with large variations of domain gap.
arXiv Detail & Related papers (2023-07-21T14:37:17Z) - One-Shot Domain Adaptive and Generalizable Semantic Segmentation with
Class-Aware Cross-Domain Transformers [96.51828911883456]
Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data.
Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation.
We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
arXiv Detail & Related papers (2022-12-14T15:54:15Z) - Multi-level Consistency Learning for Semi-supervised Domain Adaptation [85.90600060675632]
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.
We propose a Multi-level Consistency Learning framework for SSDA.
arXiv Detail & Related papers (2022-05-09T06:41:18Z) - Semi-supervised Domain Adaptation for Semantic Segmentation [3.946367634483361]
We propose a novel two-step semi-supervised dual-domain adaptation (SSDDA) approach to address both cross- and intra-domain gaps in semantic segmentation.
We demonstrate that the proposed approach outperforms state-of-the-art methods on two common synthetic-to-real semantic segmentation benchmarks.
arXiv Detail & Related papers (2021-10-20T16:13:00Z) - Source-Free Open Compound Domain Adaptation in Semantic Segmentation [99.82890571842603]
In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model.
We propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level.
Our method produces state-of-the-art results on the C-Driving dataset.
arXiv Detail & Related papers (2021-06-07T08:38:41Z) - Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain
Adaptive Semantic Segmentation [102.42638795864178]
We propose a principled meta-learning based approach to OCDA for semantic segmentation.
We cluster target domain into multiple sub-target domains by image styles, extracted in an unsupervised manner.
A meta-learner is thereafter deployed to learn to fuse sub-target domain-specific predictions, conditioned upon the style code.
We learn to online update the model by model-agnostic meta-learning (MAML) algorithm, thus to further improve generalization.
arXiv Detail & Related papers (2020-12-15T13:21:54Z) - 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)
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.