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The objective of this work is to explore how to effectively and efficiently adapt pre-trained foundation models to various downstream tasks of image semantic segmentation. Conventional methods usually fine-tuned the whole networks for each specific dataset and it was burdensome to store the massive parameters of these networks. A few recent works a...
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... each experiment, we randomly select a sample to train models and then validate them on the whole testing set. Table 3 shows the mean results and variances of different methods on two retinal segmentation datasets. We notice that those parameter-efficient fine-tuning methods are comparable to or even better than Full-Tuning under the one-shot setting, which is consistent with the findings [5,54] in the NLP field. ...Similar publications
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