23 citations · 28 across the 9 of their papers we have counts for
9 papers
FedDBP: Enhancing Federated Prototype Learning with Dual-Branch Features and Personalized Global Fusion
Ningzhi Gao, Siquan Huang, Leyu Shi +1
Federated prototype learning (FPL), as a solution to heterogeneous federated learning (HFL), effectively alleviates the challenges of data and model heterogeneity.However, existing…
BackdoorIDS: Zero-shot Backdoor Detection for Pretrained Vision Encoder
Siquan Huang, Yijiang Li, Ningzhi Gao +3
Self-supervised and multimodal vision encoders learn strong visual representations that are widely adopted in downstream vision tasks and large vision-language models (LVLMs). Howe…
SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
Ning Gao, Ngo Anh Vien, Hanna Ziesche +1
To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend pred…
Enhancing Interpretable Object Abstraction via Clustering-based Slot Initialization
Ning Gao, Bernard Hohmann, Gerhard Neumann
Object-centric representations using slots have shown the advances towards efficient, flexible and interpretable abstraction from low-level perceptual features in a compositional s…
Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term
Yun Yue, Jiadi Jiang, Zhiling Ye +3
Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking fl…
Measuring incompatibility and clustering quantum observables with a quantum switch
Ning Gao, Dantong Li, Anchit Mishra +3
The existence of incompatible observables is a cornerstone of quantum mechanics and a valuable resource in quantum technologies. Here we introduce a measure of incompatibility, cal…
Category-Agnostic 6D Pose Estimation with Conditional Neural Processes
Yumeng Li, Ning Gao, Hanna Ziesche +1
We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to ``instance-level" and ``category-level" pose estimation methods, our algorithm l…
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects
Ning Gao, Jingyu Zhang, Ruijie Chen +3
Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. I…
What Matters For Meta-Learning Vision Regression Tasks?
Ning Gao, Hanna Ziesche, Ngo Anh Vien +2
Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on r…