activity
20242026
most citedPreventing Local Pitfalls in Vector Quantization via Optimal Transport

1 citations · 1 across the 15 of their papers we have counts for

collaborators

30 papers

cs.CV2026

Vega: Learning to Drive with Natural Language Instructions

Sicheng Zuo, Yuxuan Li, Wenzhao Zheng +3

Vision-language-action models have reshaped autonomous driving to incorporate languages into the decision-making process. However, most existing pipelines only utilize the language…

cs.CV2026

DriveTok: 3D Driving Scene Tokenization for Unified Multi-View Reconstruction and Understanding

Dong Zhuo, Wenzhao Zheng, Sicheng Zuo +4

With the growing adoption of vision-language-action models and world models in autonomous driving systems, scalable image tokenization becomes crucial as the interface for the visu…

cs.CV2026

Dynamic Multimodal Activation Steering for Hallucination Mitigation in Large Vision-Language Models

Jianghao Yin, Qin Chen, Kedi Chen +3

Large Vision-Language Models (LVLMs) exhibit outstanding performance on vision-language tasks but struggle with hallucination problems. Through in-depth analysis of LVLM activation…

cs.LG2026

WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck

Haoyuan He, Yu Zheng, Jie Zhou +1

Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that…

cs.CV2026

Astra: General Interactive World Model with Autoregressive Denoising

Yixuan Zhu, Jiaqi Feng, Wenzhao Zheng +5

Recent advances in diffusion transformers have empowered video generation models to generate high-quality video clips from texts or images. However, world models with the ability t…

cs.CV2026

Moaw: Unleashing Motion Awareness for Video Diffusion Models

Tianqi Zhang, Ziyi Wang, Wenzhao Zheng +5

Video diffusion models, trained on large-scale datasets, naturally capture correspondences of shared features across frames. Recent works have exploited this property for tasks suc…