几篇论文实现代码:《Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation》(CVPR 2024) GitHub: github.com/zhang-haojie/wesam [fig4...
Segment-Anything(SAM), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot generalization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. In particular, SAM performs awkwardly on corrupted natural...
@inproceedings{zhang2024improving, title={Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation}, author={Zhang, Haojie and Su, Yongyi and Xu, Xun and Jia, Kui}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and...
Cardiac segmentationSimulating a large set of medical images with variability in anatomical representation and image appearance has the potential to provide solutions for addressing the scarcity of properly annotated data in medical image analysis research. However, due to the complexity of modeling the ...
2. Related Work Domain adaptation and generalization It is well known that significant labeling efforts are required so as to ensure the reliable performance of various tasks such as seman- tic segmentation [33, 1, 4, 64, 8]. To tackle this c...
【语义分割论文阅读】——RobustNet: Improving Domain Generalization in Urban-Scene Segmentation, 简介:论文从白化变换的角度出发解决语义分割中的域适应问题(训练和测试时,场景、天气、季节等不一样的问题)。1.理论基础1.1白化变换白化变换可参考维基百科:Whitenin
In supervised learning, unseen data usually lie in the vicinity of the training data and behave similar to the training data. However, a rugged model may make significantly different predictions, thus resulting in poor generalization performance. Here, we propose pessimistic vicinal risk minimization ...
In addition, ensemble learning has been proposed because it is more effective and has a better generalization ability than single algorithms [15]. Coupling PVAT imaging features with the ML algorithm may hold promise for improving cardiovascular prevention by enhancing early detection capabilities [16]...
Therefore, in future work, we aim to improve the model's generalization ability while ensuring its performance in detecting tiny targets on large-scale images remains stable. We also plan to enhance the backbone network of the model and further accelerate its performance. Conclusion In this paper...
However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method....