6 citations · 12 across the 7 of their papers we have counts for
8 papers
ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
Zijie Wang, Wei Zhang, Weiming Zhang +4
Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE). However, they implicitly assume that depth can be recovered as a globally smooth f…
World Models as Group Actions
Zijie Wang, Wei Zhang, Weiming Zhang +4
Video world models have achieved strong visual realism, but this does not ensure that their dynamics are truly governed by actions. In this work, we argue that action faithfulness…
OptiMVMap: Offline Vectorized Map Construction via Optimal Multi-vehicle Perspectives
Zedong Dan, Zijie Wang, Wei Zhang +6
Offline vectorized maps constitute critical infrastructure for high-precision autonomous driving and mapping services. Existing approaches rely predominantly on single ego-vehicle…
CoT4Det: A Chain-of-Thought Framework for Perception-Oriented Vision-Language Tasks
Yu Qi, Yumeng Zhang, Chenting Gong +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable success in a broad range of vision-language tasks, such as general visual question answering and optical character…
LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation
Zijie Wang, Weiming Zhang, Wei Zhang +4
Centerline graphs, crucial for path planning in autonomous driving, are traditionally learned using deterministic methods. However, these methods often lack spatial reasoning and s…
LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating
Deguo Xia, Weiming Zhang, Xiyan Liu +6
An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems.…
DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation
Deguo Xia, Weiming Zhang, Xiyan Liu +5
Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-l…
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
Chang Liu, Weiming Zhang, Xiangru Lin +6
With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that…