287 citations · 962 across the 29 of their papers we have counts for
30 papers
StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction
Tianyuan Yuan, Yicheng Liu, Yue Wang +2
High-Definition (HD) maps are essential for the safety of autonomous driving systems. While existing techniques employ camera images and onboard sensors to generate vectorized high…
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
Chenzhuang Du, Jiaye Teng, Tingle Li +5
We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which…
'Tax-free' 3DMM Conditional Face Generation
Yiwen Huang, Zhiqiu Yu, Xinjie Yi +2
3DMM conditioned face generation has gained traction due to its well-defined controllability; however, the trade-off is lower sample quality: Previous works such as DiscoFaceGAN an…
GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training
Xiaoyu Tian, Haoxi Ran, Yue Wang +1
This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without ann…
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving
Xiaoyu Tian, Tao Jiang, Longfei Yun +5
Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and…
Neural Map Prior for Autonomous Driving
Xuan Xiong, Yicheng Liu, Tianyuan Yuan +3
High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. The traditional method of creating offline HD maps involves labor-int…
ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries
Junru Gu, Chenxu Hu, Tianyuan Zhang +4
Perception and prediction are two separate modules in the existing autonomous driving systems. They interact with each other via hand-picked features such as agent bounding boxes a…
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
Tianyuan Zhang, Xuanyao Chen, Yue Wang +2
Accurate and consistent 3D tracking from multiple cameras is a key component in a vision-based autonomous driving system. It involves modeling 3D dynamic objects in complex scenes…
FUTR3D: A Unified Sensor Fusion Framework for 3D Detection
Xuanyao Chen, Tianyuan Zhang, Yue Wang +2
Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Existing multi-modal 3D detection models usually involve customized designs…
Transformer-based Network for RGB-D Saliency Detection
Yue Wang, Xu Jia, Lu Zhang +3
RGB-D saliency detection integrates information from both RGB images and depth maps to improve prediction of salient regions under challenging conditions. The key to RGB-D saliency…
Object DGCNN: 3D Object Detection using Dynamic Graphs
Yue Wang, Justin Solomon
3D object detection often involves complicated training and testing pipelines, which require substantial domain knowledge about individual datasets. Inspired by recent non-maximum…
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
Yue Wang, Vitor Guizilini, Tianyuan Zhang +3
We introduce a framework for multi-camera 3D object detection. In contrast to existing works, which estimate 3D bounding boxes directly from monocular images or use depth predictio…
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
Lu Mi, Tianxing He, Core Francisco Park +3
Latent-space interpolation is commonly used to demonstrate the generalization ability of deep latent variable models. Various algorithms have been proposed to calculate the best tr…
A framework for massive scale personalized promotion
Yitao Shen, Yue Wang, Xingyu Lu +6
Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popula…
A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM
Jiahui Fu, Qiangqiang Huang, Kevin Doherty +2
In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation ta…
Improving Multi-Modal Learning with Uni-Modal Teachers
Chenzhuang Du, Tingle Li, Yichen Liu +4
Learning multi-modal representations is an essential step towards real-world robotic applications, and various multi-modal fusion models have been developed for this purpose. Howev…
Feature-based Style Randomization for Domain Generalization
Yue Wang, Lei Qi, Yinghuan Shi +1
As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target d…
On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua, Wenxiao Wang, Zihui Xue +3
In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection
Yue Wang, Alireza Fathi, Jiajun Wu +2
A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distil…
Pillar-based Object Detection for Autonomous Driving
Yue Wang, Alireza Fathi, Abhijit Kundu +4
We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse…
Improving Factual Consistency Between a Response and Persona Facts
Mohsen Mesgar, Edwin Simpson, Iryna Gurevych
Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona. These…
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
Yonglong Tian, Yue Wang, Dilip Krishnan +2
The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost…
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification
ZiChao Dong, JiLong Wu, TingTing Ren +2
Fine-grained image classification is a challenging problem, since the difficulty of finding discriminative features. To handle this circumstance, basically, there are two ways to g…
PRNet: Self-Supervised Learning for Partial-to-Partial Registration
Yue Wang, Justin M. Solomon
We present a simple, flexible, and general framework titled Partial Registration Network (PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed lea…