22 citations · 30 across the 20 of their papers we have counts for
21 papers
Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving
Xin Zhou, Zongchuang Zhao, Zhibo Yang +13
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-langu…
SimWAM: A Simple World Action Model for End-to-End Autonomous Driving
Zongchuang Zhao, Xin Zhou, Tianyang Xu +6
In autonomous driving, World-Action Models (WAMs) have improved end-to-end planning by transferring video dynamics priors to action prediction, but many still couple planning with…
ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation
Xiao Luo, Mingyang Du, Xin Zhou +5
High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typically requires learning geometr…
HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation
Xin Zhou, Dingkang Liang, Xiwu Chen +4
Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominantly focus on future scene gen…
Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
Kaijin Chen, Dingkang Liang, Xin Zhou +4
Video world models have shown immense potential in simulating the physical world, yet existing memory mechanisms primarily treat environments as static canvases. When dynamic subje…
More Than Generation: Unifying Generation and Depth Estimation via Text-to-Image Diffusion Models
Hongkai Lin, Dingkang Liang, Mingyang Du +2
Generative depth estimation methods leverage the rich visual priors stored in pre-trained text-to-image diffusion models, demonstrating astonishing zero-shot capability. However, p…
SAM 2++: Tracking Anything at Any Granularity
Jiaming Zhang, Cheng Liang, Yichun Yang +7
Due to the varying granularity of target states across different tasks, most existing trackers are tailored to a single task, which specificity limits their generalization, prevent…
SoccerNet 2025 Challenges Results
Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez +115
The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understandi…
Less is Enough: Training-Free Video Diffusion Acceleration via Runtime-Adaptive Caching
Xin Zhou, Dingkang Liang, Kaijin Chen +7
Video generation models have demonstrated remarkable performance, yet their broader adoption remains constrained by slow inference speeds and substantial computational costs, prima…
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving
Zongchuang Zhao, Haoyu Fu, Dingkang Liang +5
The Large Visual-Language Models (LVLMs) have significantly advanced image understanding. Their comprehension and reasoning capabilities enable promising applications in autonomous…
AI-powered virtual eye: perspective, challenges and opportunities
Yue Wu, Yibo Guo, Yulong Yan +5
We envision the "virtual eye" as a next-generation, AI-powered platform that uses interconnected foundation models to simulate the eye's intricate structure and biological function…
UniFuture: A 4D Driving World Model for Future Generation and Perception
Dingkang Liang, Dingyuan Zhang, Xin Zhou +7
We present UniFuture, a unified 4D Driving World Model designed to simulate the dynamic evolution of the 3D physical world. Unlike existing driving world models that focus solely o…
The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
Sifan Tu, Xin Zhou, Dingkang Liang +4
The Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in the pursuit of autonomous driving (AD)…
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
Xin Zhou, Dingkang Liang, Sifan Tu +6
Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to inc…
MINIMA: Modality Invariant Image Matching
Jiangwei Ren, Xingyu Jiang, Zizhuo Li +3
Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the modality gap caused by different imaging systems/styles poses…
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
Dingkang Liang, Tianrui Feng, Xin Zhou +3
Recently, leveraging pre-training techniques to enhance point cloud models has become a prominent research topic. However, existing approaches typically require full fine-tuning of…
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation
Liu He, Yizhi Song, Hejun Huang +4
Text-to-video generation has been dominated by diffusion-based or autoregressive models. These novel models provide plausible versatility, but are criticized for improper physical…
A Unified Framework for 3D Scene Understanding
Wei Xu, Chunsheng Shi, Sifan Tu +3
We propose UniSeg3D, a unified 3D scene understanding framework that achieves panoptic, semantic, instance, interactive, referring, and open-vocabulary segmentation tasks within a…
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
Xin Zhou, Dingkang Liang, Wei Xu +4
Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. However, existing methods for model adaptation usually update all model pa…
PointMamba: A Simple State Space Model for Point Cloud Analysis
Dingkang Liang, Xin Zhou, Wei Xu +5
Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability. However, the attention mechanism has qu…
Diffusion-based 3D Object Detection with Random Boxes
Xin Zhou, Jinghua Hou, Tingting Yao +6
3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the…