2 citations · 2 across the 6 of their papers we have counts for
9 papers
Improving Generalized Visual Grounding with Instance-aware Joint Learning
Ming Dai, Wenxuan Cheng, Jiang-Jiang Liu +4
Generalized visual grounding tasks, including Generalized Referring Expression Comprehension (GREC) and Segmentation (GRES), extend the classical visual grounding paradigm by accom…
PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination
Ming Dai, Wenxuan Cheng, Jiedong Zhuang +4
Recent advances in visual grounding have largely shifted away from traditional proposal-based two-stage frameworks due to their inefficiency and high computational complexity, favo…
U-ViLAR: Uncertainty-Aware Visual Localization for Autonomous Driving via Differentiable Association and Registration
Xiaofan Li, Zhihao Xu, Chenming Wu +11
Accurate localization using visual information is a critical yet challenging task, especially in urban environments where nearby buildings and construction sites significantly degr…
DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback Synergy
Ming Dai, Wenxuan Cheng, Jiang-jiang Liu +4
Referring Image Segmentation (RIS) is a challenging task that aims to segment objects in an image based on natural language expressions. While prior studies have predominantly conc…
VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction
Ziyue Zhu, Shenlong Wang, Jin Xie +3
Recent advancements in camera-based occupancy prediction have focused on the simultaneous prediction of 3D semantics and scene flow, a task that presents significant challenges due…
Vision Remember: Recovering Visual Information in Efficient LVLM with Vision Feature Resampling
Ze Feng, Jiang-jiang Liu, Sen Yang +5
The computational expense of redundant vision tokens in Large Vision-Language Models (LVLMs) has led many existing methods to compress them via a vision projector. However, this co…
Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving
Lingyu Xiao, Jiang-Jiang Liu, Sen Yang +4
The autoregressive world model exhibits robust generalization capabilities in vectorized scene understanding but encounters difficulties in deriving actions due to insufficient unc…
EasyChauffeur: A Baseline Advancing Simplicity and Efficiency on Waymax
Lingyu Xiao, Jiang-Jiang Liu, Xiaoqing Ye +2
Recent advancements in deep-learning-based driving planners have primarily focused on elaborate network engineering, yielding limited improvements. This paper diverges from convent…
Exploring the Causality of End-to-End Autonomous Driving
Jiankun Li, Hao Li, Jiangjiang Liu +6
Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these model…