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cs.CV2025
Towards Reliable and Holistic Visual In-Context Learning Prompt Selection
Wenxiao Wu, Jing-Hao Xue, Chengming Xu +5
Visual In-Context Learning (VICL) has emerged as a prominent approach for adapting visual foundation models to novel tasks, by effectively exploiting contextual information embedde…
cs.CV2025
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs
Chang Wan, Ke Fan, Xinwei Sun +4
This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoretical guarantees. GANs are typical…
cs.CV2024
Robust Network Learning via Inverse Scale Variational Sparsification
Zhiling Zhou, Zirui Liu, Chengming Xu +2
While neural networks have made significant strides in many AI tasks, they remain vulnerable to a range of noise types, including natural corruptions, adversarial noise, and low-re…