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cs.CV2025

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.…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

Extrapolated Urban View Synthesis Benchmark

Xiangyu Han, Zhen Jia, Boyi Li +8

Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs). At their core is Novel View Synthesis (NVS), a crucial capabili…

cs.CV2025

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…