Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification
为解决隐私敏感监控系统中直接多类异常分类不稳定问题，提出结合联邦二值CNN门控与服务器端零样本VLM推理的两阶段架构，实现高效异常检测。
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为解决分布式、弱标签和资源受限的视频异常检测问题，提出了一种轻量级联邦多实例学习-视觉语言模型级联方法，通过冻结的VLM验证高分可疑片段。
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Near-Field Physical-Layer Authentication Under Impersonation Attacks
本文研究了近场条件下物理层认证在模仿攻击下的问题，通过最小化合法发送者与攻击者信号之间的均方误差来分析，并提出了单天线和多天线攻击者的最优预编码方案。
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GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
该研究提出GAP-Prompt方法，通过实例级自适应提示解决持续学习中的灾难性遗忘问题，实现在多个基准测试中达到最先进的性能。
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CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
This study addresses the ill-posed inverse problem in sparse-view CT reconstruction and the poor convergence of existing deep learning methods by proposing a compact deep unfolding framework inspired by second-order optimization. By constructing a structured Hessian proxy with a conjugate gradient solver and designing a global-local regularization module that integrates convolutional features with Nyström attention, the method effectively models image priors. Experiments on AAPM and DeepLesion datasets demonstrate stable convergence, significant noise power reduction, and enhanced visual fidelity. Achieving superior quantitative metrics compared to state-of-the-art approaches, this work provides an efficient and reliable solution for sparse-view CT reconstruction.
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Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification
为解决隐私敏感监控系统中直接多类异常分类不稳定问题，提出结合联邦二值CNN门控与服务器端零样本VLM推理的两阶段架构，实现高效异常检测。
0 citationsRead paper
Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
为解决分布式、弱标签和资源受限的视频异常检测问题，提出了一种轻量级联邦多实例学习-视觉语言模型级联方法，通过冻结的VLM验证高分可疑片段。
0 citationsRead paper
Near-Field Physical-Layer Authentication Under Impersonation Attacks
本文研究了近场条件下物理层认证在模仿攻击下的问题，通过最小化合法发送者与攻击者信号之间的均方误差来分析，并提出了单天线和多天线攻击者的最优预编码方案。
0 citationsRead paper
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
该研究提出GAP-Prompt方法，通过实例级自适应提示解决持续学习中的灾难性遗忘问题，实现在多个基准测试中达到最先进的性能。
0 citationsRead paper
CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
This study addresses the ill-posed inverse problem in sparse-view CT reconstruction and the poor convergence of existing deep learning methods by proposing a compact deep unfolding framework inspired by second-order optimization. By constructing a structured Hessian proxy with a conjugate gradient solver and designing a global-local regularization module that integrates convolutional features with Nyström attention, the method effectively models image priors. Experiments on AAPM and DeepLesion datasets demonstrate stable convergence, significant noise power reduction, and enhanced visual fidelity. Achieving superior quantitative metrics compared to state-of-the-art approaches, this work provides an efficient and reliable solution for sparse-view CT reconstruction.
0 citationsRead paper