Author

Jingyong Su

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Sep 2026

Combating Overfitting of Adversarial Training Efficiently via Balanced Instance Adaptive Defense

Adversarial training, one of the most effective methods for enhancing neural network robustness, is typically formulated as a min-max game between an attacker and a defender. Despite its success, most adversarial training methods suffer from robust overfitting, leading to a significant gap in robustness between the tra...

Xin-Yue Zhang, Shaocong Wu, Qiben Shan et al. · IEEE Transactions on Image P... · 0 citations

Sep 2026

Enhancing Local Cognition of CLIP for Training-Free Open Vocabulary Semantic Segmentation.

CLIP, as a vision-language model, has significantly advanced Open-Vocabulary Semantic Segmentation (OVSS) with its zero-shot generalization. Despite its success, its application to OVSS is limited due to its initial image-level alignment training, which affects its performance in tasks requiring detailed local context....

Tong Shao, Zhuo-Tao Tian, Yun-Yang Mo et al. · IEEE Transactions on Pattern... · 0 citations