4 citations · 9 across the 12 of their papers we have counts for
12 papers
BitC-3DGS: High-Capacity 3D Gaussian Splatting Watermarking via Bit Compression
Yuquan Bi, Baosheng Yu, Yingke Lei +5
High-capacity watermarking is necessary for 3D Gaussian Splatting (3DGS) assets to embed rich information (e.g., ownership, provenance, and authentication codes), enabling reliable…
Toward Universal Skeleton-Based Action Recognition across Heterogeneous Skeletons and Open Vocabularies
Jidong Kuang, Hongsong Wang, Jie Gui +2
Skeleton data used for action recognition are acquired from a wide range of sources, including depth sensors, marker-based motion capture systems, and 2D/3D pose estimators. These…
Toward Fine-Grained Facial Control in 3D Talking Head Generation
Shaoyang Xie, Xiaofeng Cong, Baosheng Yu +4
Audio-driven talking head generation is a core component of digital avatars, and 3D Gaussian Splatting has shown strong performance in real-time rendering of high-fidelity talking…
Revisiting Adversarial Training under Hyperspectral Image
Weihua Zhang, Chengze Jiang, Minjing Dong +5
Recent studies have shown that deep learning-based hyperspectral image (HSI) classification models are highly vulnerable to adversarial attacks, posing significant security risks.…
Efficient Diffusion-Based 3D Human Pose Estimation with Hierarchical Temporal Pruning
Yuquan Bi, Hongsong Wang, Xinli Shi +3
Diffusion models have demonstrated strong capabilities in generating high-fidelity 3D human poses, yet their iterative nature and multi-hypothesis requirements incur substantial co…
A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions
Shuxian Zhao, Jie Gui, Minjing Dong +5
The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distri…
Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation
Xiaofeng Cong, Jing Zhang, Yeying Jin +5
Underwater images often suffer from quality degradation due to absorption and scattering effects. Most existing underwater image enhancement algorithms produce a single, fixed-colo…
Improving Fast Adversarial Training via Self-Knowledge Guidance
Chengze Jiang, Junkai Wang, Minjing Dong +5
Adversarial training has achieved remarkable advancements in defending against adversarial attacks. Among them, fast adversarial training (FAT) is gaining attention for its ability…
Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
Jie Gui, Chengze Jiang, Minjing Dong +4
While adversarial training is an effective defense method against adversarial attacks, it notably increases the training cost. To this end, fast adversarial training (FAT) is prese…
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models
Siyu Zhai, Zhibo He, Xiaofeng Cong +6
Learning-based methods for underwater image enhancement (UWIE) have undergone extensive exploration. However, learning-based models are usually vulnerable to adversarial examples s…
Fooling the Image Dehazing Models by First Order Gradient
Jie Gui, Xiaofeng Cong, Chengwei Peng +2
The research on the single image dehazing task has been widely explored. However, as far as we know, no comprehensive study has been conducted on the robustness of the well-trained…
Exploring the Coordination of Frequency and Attention in Masked Image Modeling
Jie Gui, Tuo Chen, Minjing Dong +4
Recently, masked image modeling (MIM), which learns visual representations by reconstructing the masked patches of an image, has dominated self-supervised learning in computer visi…