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20242026
most citedTokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

2 citations · 6 across the 17 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens

Xiaocheng Lu, Yuxi Chen, Jie Zhang +5

Image tokenizers, from 2D grids to recent 1D sequences, typically encode every image with the same fixed number of tokens. Yet visual complexity is highly heterogeneous, so a unifo…

cs.CV2026

TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models

Junzhe Chen, Siyuan Meng, Yuxi Chen +3

Video large language models (Video-LLMs) have made strong progress in general video understanding, but their ability to maintain temporal object consistency remains underexplored.…

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

Latent Chain-of-Thought World Modeling for End-to-End Driving

Shuhan Tan, Kashyap Chitta, Yuxiao Chen +8

Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most…

cs.CV2025

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…

cs.CV2024★ 1 cited

STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes

Jiawei Yang, Jiahui Huang, Yuxiao Chen +10

We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often…