In-Situ Reconstruction of the International Space Station Using 3D Gaussian Splatting and Astrobee
本文使用3D高斯点云技术，基于Astrobee机器人采集的图像，实现了国际空间站内部的高效3D重建与映射。
0 citationsRead paper
Selected work
Representative Papers
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
This work addresses the insufficient robustness of deep neural networks in safety-critical scenarios caused by rare semantic shifts and the difficulty of verifying high-level semantic requirements. To tackle these challenges, the authors propose SeFaR, a framework that integrates diffusion models with vision-language models to generate realistic, semantically consistent, and diverse perturbations guided by natural language specifications and valid inputs. SeFaR employs a hierarchical concept model to systematically explore the feature space, incorporates domain knowledge through user-defined concepts, and leverages a feedback mechanism to identify requirement-irrelevant features that influence model decisions. This enables the generation of interpretable failure-inducing semantic concepts along with corresponding test samples. Experimental results demonstrate that SeFaR effectively uncovers model failures and accurately attributes them to specific semantic factors.
0 citationsRead paper
Recent publications
Latest Papers
In-Situ Reconstruction of the International Space Station Using 3D Gaussian Splatting and Astrobee
本文使用3D高斯点云技术，基于Astrobee机器人采集的图像，实现了国际空间站内部的高效3D重建与映射。
0 citationsRead paper
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
This work addresses the insufficient robustness of deep neural networks in safety-critical scenarios caused by rare semantic shifts and the difficulty of verifying high-level semantic requirements. To tackle these challenges, the authors propose SeFaR, a framework that integrates diffusion models with vision-language models to generate realistic, semantically consistent, and diverse perturbations guided by natural language specifications and valid inputs. SeFaR employs a hierarchical concept model to systematically explore the feature space, incorporates domain knowledge through user-defined concepts, and leverages a feedback mechanism to identify requirement-irrelevant features that influence model decisions. This enables the generation of interpretable failure-inducing semantic concepts along with corresponding test samples. Experimental results demonstrate that SeFaR effectively uncovers model failures and accurately attributes them to specific semantic factors.
0 citationsRead paper