23 citations · 28 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…