233 citations · 1.1k across the 34 of their papers we have counts for
35 papers
Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene
Tai-Yu Pan, Sooyoung Jeon, Mengdi Fan +6
Self-driving cars relying solely on ego-centric perception face limitations in sensing, often failing to detect occluded, faraway objects. Collaborative autonomous driving (CAV) se…
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu +9
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X data…
Learning 3D Perception from Others' Predictions
Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan +7
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs re…
DiffuBox: Refining 3D Object Detection with Point Diffusion
Xiangyu Chen, Zhenzhen Liu, Katie Z Luo +10
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining h…
Better Monocular 3D Detectors with LiDAR from the Past
Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz +5
Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their wid…
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen +7
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we in…
Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
Travis Zhang, Katie Luo, Cheng Perng Phoo +5
The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving…
Gradual Domain Adaptation without Indexed Intermediate Domains
Hong-You Chen, Wei-Lun Chao
The effectiveness of unsupervised domain adaptation degrades when there is a large discrepancy between the source and target domains. Gradual domain adaptation (GDA) is one promisi…
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Yurong You, Katie Z Luo, Xiangyu Chen +6
Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely. Small, far-away, or highly occluded objects are particularly chall…
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
Tai-Yu Pan, Cheng Zhang, Yandong Li +5
Vanilla models for object detection and instance segmentation suffer from the heavy bias toward detecting frequent objects in the long-tailed setting. Existing methods address this…
On Bridging Generic and Personalized Federated Learning for Image Classification
Hong-You Chen, Wei-Lun Chao
Federated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributio…
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Hong-You Chen, Wei-Lun Chao
Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate l…
Wasserstein Distances for Stereo Disparity Estimation
Divyansh Garg, Yan Wang, Bharath Hariharan +3
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or dispar…
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
Yan Wang, Xiangyu Chen, Yurong You +5
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike. While deep networks are great…
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Rui Qian, Divyansh Garg, Yan Wang +6
Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they…
Interactive Natural Language-based Person Search
Vikram Shree, Wei-Lun Chao, Mark Campbell
In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions. Specifically, we study how to systematically design a…
Revisiting Meta-Learning as Supervised Learning
Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan +2
Recent years have witnessed an abundance of new publications and approaches on meta-learning. This community-wide enthusiasm has sparked great insights but has also created a pleth…
An Empirical Study of Person Re-Identification with Attributes
Vikram Shree, Wei-Lun Chao, Mark Campbell
Person re-identification aims to identify a person from an image collection, given one image of that person as the query. There is, however, a plethora of real-life scenarios where…
Visual Question Answering on 360° Images
Shih-Han Chou, Wei-Lun Chao, Wei-Sheng Lai +2
In this work, we introduce VQA 360, a novel task of visual question answering on 360 images. Unlike a normal field-of-view image, a 360 image captures the entire visual content aro…
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan +1
Classifiers trained with class-imbalanced data are known to perform poorly on test data of the "minor" classes, of which we have insufficient training data. In this paper, we inves…
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger +1
Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-lear…
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
Brian H. Wang, Wei-Lun Chao, Yan Wang +3
Object segmentation in three-dimensional (3-D) point clouds is a critical task for robots capable of 3-D perception. Despite the impressive performance of deep learning-based appro…
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Tao Yu, Shengyuan Hu, Chuan Guo +2
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial…
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering
Cheng Zhang, Wei-Lun Chao, Dong Xuan
Visual question answering (Visual QA) has attracted significant attention these years. While a variety of algorithms have been proposed, most of them are built upon different combi…