Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation

Jintao Tong, Ran Ma, Yixiong Zou, Guangyao Chen, Yuhua Li, Ruixuan Li
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:59829-59845, 2025.

Abstract

Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain datasets where only a few training samples are available for efficient finetuning. There are majorly two challenges in this task: (1) the domain gap and (2) finetuning with scarce data. To solve these challenges, we revisit the adapter-based methods, and discover an intriguing insight not explored in previous works: the adapter not only helps the fine-tuning of downstream tasks but also naturally serves as a domain information decoupler. Then, we delve into this finding for an interpretation, and we find the model’s inherent structure could lead to a natural decoupling of domain information. Building upon this insight, we propose the Domain Feature Navigator (DFN), which is a structure-based decoupler instead of loss-based ones like current works, to capture domain-specific information, thereby directing the model’s attention towards domain-agnostic knowledge. Moreover, to prevent the potential excessive overfitting of DFN during the source-domain training, we further design the SAM-SVN method to constrain DFN from learning sample-specific knowledge. On target domains, we freeze the model and fine-tune the DFN to learn knowledge specific to target domains. Extensive experiments demonstrate that our method surpasses the state-of-the-art method in CD-FSS significantly by 2.69% and 4.68% average MIoU in 1-shot and 5-shot scenarios, respectively.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-tong25b, title = {Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation}, author = {Tong, Jintao and Ma, Ran and Zou, Yixiong and Chen, Guangyao and Li, Yuhua and Li, Ruixuan}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {59829--59845}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/tong25b/tong25b.pdf}, url = {https://proceedings.mlr.press/v267/tong25b.html}, abstract = {Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain datasets where only a few training samples are available for efficient finetuning. There are majorly two challenges in this task: (1) the domain gap and (2) finetuning with scarce data. To solve these challenges, we revisit the adapter-based methods, and discover an intriguing insight not explored in previous works: the adapter not only helps the fine-tuning of downstream tasks but also naturally serves as a domain information decoupler. Then, we delve into this finding for an interpretation, and we find the model’s inherent structure could lead to a natural decoupling of domain information. Building upon this insight, we propose the Domain Feature Navigator (DFN), which is a structure-based decoupler instead of loss-based ones like current works, to capture domain-specific information, thereby directing the model’s attention towards domain-agnostic knowledge. Moreover, to prevent the potential excessive overfitting of DFN during the source-domain training, we further design the SAM-SVN method to constrain DFN from learning sample-specific knowledge. On target domains, we freeze the model and fine-tune the DFN to learn knowledge specific to target domains. Extensive experiments demonstrate that our method surpasses the state-of-the-art method in CD-FSS significantly by 2.69% and 4.68% average MIoU in 1-shot and 5-shot scenarios, respectively.} }
Endnote
%0 Conference Paper %T Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation %A Jintao Tong %A Ran Ma %A Yixiong Zou %A Guangyao Chen %A Yuhua Li %A Ruixuan Li %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-tong25b %I PMLR %P 59829--59845 %U https://proceedings.mlr.press/v267/tong25b.html %V 267 %X Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain datasets where only a few training samples are available for efficient finetuning. There are majorly two challenges in this task: (1) the domain gap and (2) finetuning with scarce data. To solve these challenges, we revisit the adapter-based methods, and discover an intriguing insight not explored in previous works: the adapter not only helps the fine-tuning of downstream tasks but also naturally serves as a domain information decoupler. Then, we delve into this finding for an interpretation, and we find the model’s inherent structure could lead to a natural decoupling of domain information. Building upon this insight, we propose the Domain Feature Navigator (DFN), which is a structure-based decoupler instead of loss-based ones like current works, to capture domain-specific information, thereby directing the model’s attention towards domain-agnostic knowledge. Moreover, to prevent the potential excessive overfitting of DFN during the source-domain training, we further design the SAM-SVN method to constrain DFN from learning sample-specific knowledge. On target domains, we freeze the model and fine-tune the DFN to learn knowledge specific to target domains. Extensive experiments demonstrate that our method surpasses the state-of-the-art method in CD-FSS significantly by 2.69% and 4.68% average MIoU in 1-shot and 5-shot scenarios, respectively.
APA
Tong, J., Ma, R., Zou, Y., Chen, G., Li, Y. & Li, R.. (2025). Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:59829-59845 Available from https://proceedings.mlr.press/v267/tong25b.html.

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