Self-Training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation
מושגי ליבה
A novel self-training approach that incorporates a mean-teacher model and a reliability metric learned through proxy-based metric learning to effectively adapt a pre-trained source model to the target domain without access to source data.
תקציר
The paper proposes a self-training approach called Self-Training via Metric Learning (STvM) for source-free domain adaptation of semantic segmentation. The key aspects are:
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Mean-Teacher Model: The approach utilizes a mean-teacher model, where the teacher network generates pseudo-labels and the student network is trained using these pseudo-labels. The teacher network's parameters are updated as a moving average of the student network's parameters.
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Reliability Metric: To address the challenge of assessing the reliability of predictions in the target domain, the authors introduce a novel reliability metric learned through proxy-based metric learning. This metric estimates the similarity between the pixel features and the class prototypes in the target domain.
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Gradient Scaling: Instead of filtering out predictions based on confidence thresholds, the proposed method scales the gradients computed from pseudo-labels based on the reliability metric. This allows the model to utilize all predictions while suppressing the impact of unreliable ones.
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Metric-based Online ClassMix: To effectively apply mixing augmentation in the absence of labeled data, the authors propose a method called Metric-based Online ClassMix (MOCM). It samples reliable patches from the target domain based on the metric similarity and applies mixing augmentation.
The proposed STvM approach demonstrates superior performance compared to state-of-the-art source-free domain adaptation methods for semantic segmentation on various benchmark datasets, including GTA5-to-CityScapes, SYNTHIA-to-CityScapes, and CityScapes-to-NTHU Cross-City.
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation
סטטיסטיקה
The paper reports the following key statistics:
The GTA5 dataset contains 24,966 synthetic images with 33 categories, and the 19 classes compatible with Cityscapes are used.
The SYNTHIA dataset contains 9,400 images with 16 shared categories with Cityscapes.
The CityScapes-to-NTHU Cross-City dataset has 3,200 unlabeled training images and 100 labeled test images with 13 shared classes with Cityscapes.
ציטוטים
"Unlike conventional methods that filter predictions for pseudo-labels, we employ all predictions in the self-training process and adjust the gradients based on the reliability metric."
"We devise an effective way to adapt mixing augmentation, dubbed as a metric-based online ClassMix, in the absence of labeled data by leveraging the reliability metric to sample patches from the target domain."
שאלות מעמיקות
How can the proposed reliability metric be extended to other domain adaptation tasks beyond semantic segmentation
The proposed reliability metric based on proxy-based metric learning can be extended to other domain adaptation tasks beyond semantic segmentation by adapting it to different types of dense prediction tasks. For instance, in tasks like object detection or instance segmentation, the reliability metric can be utilized to assess the confidence of predictions and adjust the training process accordingly. By training a metric network to learn distance metrics for different classes or instances, the reliability metric can help in determining the reliability of predictions and guiding the training process effectively. This approach can be applied to various domain adaptation scenarios where the source and target domains have different distributions, enabling the model to adapt to new data distributions without labeled data from the target domain.
What are the potential limitations of the proxy-based metric learning approach used in the reliability metric, and how could it be further improved
One potential limitation of the proxy-based metric learning approach used in the reliability metric is the sensitivity to the quality and representativeness of the proxy features. If the proxy features do not accurately capture the class distribution or if they are not updated effectively during training, it can lead to suboptimal performance of the reliability metric. To address this limitation and improve the approach, it is essential to carefully design the proxy features to be representative of the class distribution in the target domain. Additionally, incorporating techniques for adaptive updating of the proxy features based on the training data dynamics can enhance the robustness and effectiveness of the reliability metric. Regular monitoring and fine-tuning of the proxy features during training can help mitigate potential limitations and improve the overall performance of the metric learning approach.
Can the STvM framework be applied to other dense prediction tasks, such as object detection or instance segmentation, and what modifications would be required
The STvM framework can be applied to other dense prediction tasks, such as object detection or instance segmentation, with certain modifications to adapt to the specific requirements of these tasks. For object detection, the framework can be extended by incorporating region proposal networks (RPNs) to generate candidate object regions and refine the predictions based on the reliability metric. In the case of instance segmentation, the framework can be modified to handle pixel-wise instance segmentation masks and adjust the training process based on the reliability of instance predictions. Additionally, for both tasks, the augmentation strategies and loss functions may need to be tailored to the specific characteristics of object detection and instance segmentation. By customizing the framework to suit the requirements of these tasks, STvM can be effectively applied to a wide range of dense prediction tasks beyond semantic segmentation.
