From the 1 of 17 linked papers with an AI index.
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Domain Generalization via Discrete Codebook Learning
Shaocong Long, Qianyu Zhou, Xikun Jiang +3
Domain generalization (DG) strives to address distribution shifts across diverse environments to enhance model's generalizability. Current DG approaches are confined to acquiring r…
Generative Classifier for Domain Generalization
Shaocong Long, Qianyu Zhou, Xiangtai Li +5
Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods focus on learning domain invariance,…
Diverse Target and Contribution Scheduling for Domain Generalization
Shaocong Long, Qianyu Zhou, Chenhao Ying +2
Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets…
DGMamba: Domain Generalization via Generalized State Space Model
Shaocong Long, Qianyu Zhou, Xiangtai Li +5
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers…
Rethinking Domain Generalization: Discriminability and Generalizability
Shaocong Long, Qianyu Zhou, Chenhao Ying +2
Domain generalization(DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques te…