1 citations · 3 across the 11 of their papers we have counts for
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Adaptive Causal Alignment for High-Confidence Adversarial Training
Zhiming Luo, Kejia Zhang, Yingxin Lai +3
Inverse adversarial training leverages high-confidence predictions to stabilize robust learning, yet we uncover a critical paradox: high confidence often stems from overfitting to…
HCCM: Hierarchical Cross-Granularity Contrastive and Matching Learning for Natural Language-Guided Drones
Hao Ruan, Jinliang Lin, Yingxin Lai +2
Natural Language-Guided Drones (NLGD) provide a novel paradigm for tasks such as target matching and navigation. However, the wide field of view and complex compositional semantics…
Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
Yongcun Zhang, Jiajun Xu, Yina He +3
Tongue diagnosis in Traditional Chinese Medicine (TCM) is a crucial diagnostic method that can reflect an individual's health status. Traditional methods for identifying tooth-mark…
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to Tail
Yina He, Lei Peng, Yongcun Zhang +3
Current out-of-distribution (OOD) detection methods typically assume balanced in-distribution (ID) data, while most real-world data follow a long-tailed distribution. Previous appr…
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment
Kejia Zhang, Juanjuan Weng, Shaozi Li +1
Despite the remarkable progress of deep neural networks (DNNs) in various visual tasks, their vulnerability to adversarial examples raises significant security concerns. Recent adv…
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness
Kejia Zhang, Juanjuan Weng, Yuanzheng Cai +2
Ensuring the robustness of deep neural networks against adversarial attacks remains a fundamental challenge in computer vision. While adversarial training (AT) has emerged as a pro…
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness
Kejia Zhang, Juanjuan Weng, Junwei Wu +3
The vulnerability of Deep Neural Networks to adversarial perturbations presents significant security concerns, as the imperceptible perturbations can contaminate the feature space…
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing
Juanjuan Weng, Zhiming Luo, Shaozi Li
This paper aims to enhance the transferability of adversarial samples in targeted attacks, where attack success rates remain comparatively low. To achieve this objective, we propos…
Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability
Juanjuan Weng, Zhiming Luo, Shaozi Li
Recent studies have shown that Deep Neural Networks (DNNs) are susceptible to adversarial attacks, with frequency-domain analysis underscoring the significance of high-frequency co…
Selective Domain-Invariant Feature for Generalizable Deepfake Detection
Yingxin Lai, Guoqing Yang Yifan He, Zhiming Luo +1
With diverse presentation forgery methods emerging continually, detecting the authenticity of images has drawn growing attention. Although existing methods have achieved impressive…
A brief introduction to a framework named Multilevel Guidance-Exploration Network
Guoqing Yang, Zhiming Luo, Jianzhe Gao +4
Human behavior anomaly detection aims to identify unusual human actions, playing a crucial role in intelligent surveillance and other areas. The current mainstream methods still ad…
Zero-Shot Co-salient Object Detection Framework
Haoke Xiao, Lv Tang, Bo Li +2
Co-salient Object Detection (CoSOD) endeavors to replicate the human visual system's capacity to recognize common and salient objects within a collection of images. Despite recent…