18 citations · 26 across the 18 of their papers we have counts for
8 papers · 1 filter
Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning
Zhenghao "Mark" Peng, Wenhao Ding, Yurong You +11
Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet…
Towards Efficient and Effective Multi-Camera Encoding for End-to-End Driving
Jiawei Yang, Ziyu Chen, Yurong You +7
We present Flex, an efficient and effective scene encoder that addresses the computational bottleneck of processing high-volume multi-camera data in end-to-end autonomous driving.…
FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos
Yulu Gan, Ligeng Zhu, Dandan Shan +8
Motion understanding is fundamental to physical reasoning, enabling models to infer dynamics and predict future states. However, state-of-the-art models still struggle on recent mo…
Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation
Yusuke Hirota, Ryo Hachiuma, Boyi Li +9
Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-worl…
The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving
Jay Patrikar, Apoorva Sharma, Sushant Veer +3
Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisel…
LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Yusuke Hirota, Boyi Li, Ryo Hachiuma +7
Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating de…
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Wei Chow, Jiageng Mao, Boyi Li +3
Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. Whi…
DreamDrive: Generative 4D Scene Modeling from Street View Images
Jiageng Mao, Boyi Li, Boris Ivanovic +7
Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a critical step towards scalable training of self-driving models. Reconstruction-based…