Unsupervised Model Adaptation for Continual Semantic Segmentation
By Serban Stan, Mohammad Rostami. 2020
71 citations recorded by the source corpus.
We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. A similar problem has been studied extensively in the unsupervised domain adaptation (UDA) literature, but existing UDA algorithms require access to both the source domain labeled data and the target domain unlabeled data for training a domain agnostic semantic segmentation model. Relaxing this constraint enables a user to adapt pretrained models to generalize in a target domain, without requiring access to source data. To this end, we learn a prototypical distribution for the source domain in an intermediate embedding space. This distribution encodes the abstract knowledge that is learned from the source domain. We then use this distribution for aligning the target domain distribution with the source domain distribution in the embedding space. We provide theoretical analysis and explain conditions under which our algorithm is effective. Experiments on benchmark adaptation tasks demonstrate our method achieves competitive performance even compared with joint UDA approaches.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Meta & Continual Learning
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Citations in the corpus
- A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “In addition to the regular CSS, other relevant settings include unsupervised domain adaptation for CSS [394], where a pre-trained model adapts to the unannotated data of the target domain without using the annotated data of the source domain; incremental few-shot semantic segmentation [379], which p”
- Representation Compensation Networks for Continual Semantic Segmentation (2022, background): “While others [31, 78, 95] utilize high-dimensional information, self-training and model adaptation to overcome this problem.”
- Detection and Continual Learning of Novel Face Presentation Attacks (2021, methodology): “In the case of a deep neural network with good generalization performance, the embedding space should be discriminative and the data representation would form a bimodal distribution [35], similar to the visualization presented in Figure 2.”
- Class-Incremental Learning: A Survey (2023, background): “Recent works in CSS also involve incremental learning in more challenging scenarios, e.g. , learning with few-shot annotations [283], [284], different domains [285], [286], continual instance segmentation [287], [288] and weakly semantic segmentation [289].”
- A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts (2023, unknown): “Besides, a certain number of following methods [103, 178, 193] further employ advanced domain adversarial training strategies to achieve better adaptation.”