Unsupervised Domain Adaptation For Semantic Segmentation Research Articles

EDRL: Entropy-guided disentangled representation learning for unsupervised domain adaptation in semantic segmentation

Background and ObjectiveDeep learning-based approaches are excellent at learning from large amounts of data, but can be poor at generalizing the learned knowledge to testing datasets with domain shift, i.e., when there exists distribution discrepancy between the training dataset (source domain) and the testing dataset (target domain). In this paper, we investigate unsupervised domain adaptation (UDA) techniques to train a cross-domain segmentation method which is robust to domain shift, eliminating the requirement of any annotations on the target domain. MethodsTo this end, we propose an Entropy-guided Disentangled Representation Learning, referred as EDRL, for UDA in semantic segmentation. Concretely, we synergistically integrate image alignment via disentangled representation learning with feature alignment via entropy-based adversarial learning into one network, which is trained end-to-end. We additionally introduce a dynamic feature selection mechanism via soft gating, which helps to further enhance the task-specific feature alignment. We validate the proposed method on two publicly available datasets: the CT-MR dataset and the multi-sequence cardiac MR (MS-CMR) dataset. ResultsOn both datasets, our method achieved better results than the state-of-the-art (SOTA) methods. Specifically, on the CT-MR dataset, our method achieved an average DSC of 84.8% when taking CT as the source domain and MR as the target domain, and an average DSC of 84.0% when taking MR as the source domain and CT as the target domain. ConclusionsResults from comprehensive experiments demonstrate the efficacy of the proposed EDRL model for cross-domain medical image segmentation.

Threshold-Adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation

Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning based methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with synthetic datasets will significantly degenerate in the actual scenes. Unsupervised domain adaptation (UDA) for semantic segmentation is used to reduce the domain gap and improve the performance on the target domain. Existing adversarial-based and self-training methods usually involve complex training procedures, while entropy-based methods have recently received attention for their simplicity and effectiveness. However, entropy-based UDA methods have problems that they barely optimize hard samples and lack an explicit semantic connection between the source and target domains. In this paper, we propose a novel two-stage entropy-based UDA method for semantic segmentation. In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain. It first introduces unsupervised focal loss into UDA for semantic segmentation, helping to optimize hard samples and avoiding generating unreliable pseudo-labels in the target domain. In stage two, we employ cross-domain image mixing (CIM) to bridge the semantic knowledge between two domains and incorporate long-tail class pasting to alleviate the class imbalance problem. Extensive experiments on synthetic-to-real and cross-city benchmarks demonstrate the effectiveness of our method. It achieves state-of-the-art performance using DeepLabV2, as well as competitive performance using the lightweight BiSeNet with great advantages in training and inference time.