1 citations · 1 across the 4 of their papers we have counts for
8 papers
EDCT-Bench: Uncovering Faithfulness Gaps in VLMs via Explanation-Driven Counterfactual Testing
Sihao Ding, Santosh Vasa, Aditi Ramadwar +1
Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanati…
BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations
Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen +1
The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are es…
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Thomas Monninger, Zihan Zhang, Steffen Staab +1
Accurate environmental representations are essential for autonomous driving, providing the foundation for safe and efficient navigation. Traditionally, high-definition (HD) maps ar…
Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Fengzhi Guo, Chih-Chuan Hsu, Sihao Ding +1
Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Sp…
Explanation-Driven Counterfactual Testing for Faithfulness in Vision-Language Model Explanations
Sihao Ding, Santosh Vasa, Aditi Ramadwar
Vision-Language Models (VLMs) often produce fluent Natural Language Explanations (NLEs) that sound convincing but may not reflect the causal factors driving predictions. This misma…
MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving
Thomas Monninger, Zihan Zhang, Zhipeng Mo +3
Autonomous driving requires an understanding of the static environment from sensor data. Learned Bird's-Eye View (BEV) encoders are commonly used to fuse multiple inputs, and a vec…
AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
Santosh Vasa, Aditi Ramadwar, Jnana Rama Krishna Darabattula +5
Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple…
AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction
Thomas Monninger, Md Zafar Anwar, Stanislaw Antol +2
Autonomous driving requires understanding infrastructure elements, such as lanes and crosswalks. To navigate safely, this understanding must be derived from sensor data in real-tim…