1 citations · 2 across the 9 of their papers we have counts for
9 papers
Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation
Shulei Ji, Zihao Wang, Jiaxing Yu +4
Video-to-music (V2M) generation aims to create music that aligns with visual content. However, two main challenges persist in existing methods: (1) the lack of explicit rhythm mode…
Object Style Diffusion for Generalized Object Detection in Urban Scene
Hao Li, Xiangyuan Yang, Mengzhu Wang +4
Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based app…
Adversarial Detection with a Dynamically Stable System
Xiaowei Long, Jie Lin, Xiangyuan Yang
Adversarial detection is designed to identify and reject maliciously crafted adversarial examples(AEs) which are generated to disrupt the classification of target models. Presently…
Fuzziness-tuned: Improving the Transferability of Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments
Zhaoxi Mu, Xinyu Yang, Xiangyuan Yang +1
In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized f…
Improving the Transferability of Adversarial Examples via Direction Tuning
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models. Although conside…
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model aga…
FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
In strong adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the output features of…
Gradient Aligned Attacks via a Few Queries
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks…