16 citations · 42 across the 14 of their papers we have counts for
17 papers
Reprogramming Vision-Language Models via Structured Prompt Reparameterization
Zizhao Li, Chengyi Cai, Mohammed Yaqoob Ansari +3
Visual reprogramming adapts pretrained models to downstream tasks by modifying their input and output interfaces while keeping the backbone fixed. In vision-language models, existi…
You Only Reprogram Once: Rethinking Prolonged Training for Visual Reprogramming
Zizhao Li, Mohammed Yaqoob Ansari, Xinyu Su +3
Visual reprogramming is a parameter-efficient method for adapting pretrained models, yet its training can remain computationally expensive: even with a frozen backbone, visual prom…
Relative Energy Learning for LiDAR Out-of-Distribution Detection
Zizhao Li, Zhengkang Xiang, Jiayang Ao +2
Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles and unexpected objects beyond the…
rareboost3d: a synthetic lidar dataset with enhanced rare classes
Shutong Lin, Zhengkang Xiang, Jianzhong Qi +1
Real-world point cloud datasets have made significant contributions to the development of LiDAR-based perception technologies, such as object segmentation for autonomous driving. H…
Mapping Hidden Heritage: Self-supervised Pre-training on High-Resolution LiDAR DEM Derivatives for Archaeological Stone Wall Detection
Zexian Huang, Mashnoon Islam, Brian Armstrong +3
Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management du…
Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion
Xueyang Kang, Zhengkang Xiang, Zezheng Zhang +1
Novel view synthesis (NVS) from a single image is highly ill-posed due to large unobserved regions, especially for views that deviate significantly from the input. While existing m…
Surfel-based 3D Registration with Equivariant SE(3) Features
Xueyang Kang, Hang Zhao, Kourosh Khoshelham +1
Point cloud registration is crucial for ensuring 3D alignment consistency of multiple local point clouds in 3D reconstruction for remote sensing or digital heritage. While various…
Out-of-distribution detection in 3D applications: a review
Zizhao Li, Xueyang Kang, Joseph West +1
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods fo…
SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusion
Zhengkang Xiang, Zizhao Li, Amir Khodabandeh +1
Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversit…
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects
Zizhao Li, Zhengkang Xiang, Joseph West +1
Traditional object detection methods operate under the closed-set assumption, where models can only detect a fixed number of objects predefined in the training set. Recent works on…
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
Xueyang Kang, Zhaoliang Luan, Kourosh Khoshelham +1
Point cloud registration is a foundational task for 3D alignment and reconstruction applications. While both traditional and learning-based registration approaches have succeeded,…
Learning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network
Zexian Huang, Kourosh Khoshelham, Martin Tomko
Geometric shape classification of vector polygons remains a challenging task in spatial analysis. Previous studies have primarily focused on deep learning approaches for rasterized…
LMSeg: An end-to-end geometric message-passing network on barycentric dual graphs for large-scale landscape mesh segmentation
Zexian Huang, Kourosh Khoshelham, Martin Tomko
Semantic segmentation of large-scale 3D landscape meshes is critical for geospatial analysis in complex environments, yet existing approaches face persistent challenges of scalabil…
FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack
Xueyang Kang, Fengze Han, Abdur R. Fayjie +3
Most existing methods for depth estimation from a focal stack of images employ convolutional neural networks (CNNs) using 2D or 3D convolutions over a fixed set of images. However,…
Centimeter-level Positioning by Instantaneous Lidar-aided GNSS Ambiguity Resolution
Junjie Zhang, Amir Khodabandeh, Kourosh Khoshelham
High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurement…
Fast and Reliable WiFi Fingerprint Collection for Indoor Localization
Fuqiang Gu, Milad Ramezani, Kourosh Khoshelham +3
Fingerprinting is a popular indoor localization technique since it can utilize existing infrastructures (e.g., access points). However, its site survey process is a labor-intensive…
Detecting Unsigned Physical Road Incidents from Driver-View Images
Alex Levering, Martin Tomko, Devis Tuia +1
Safety on roads is of uttermost importance, especially in the context of autonomous vehicles. A critical need is to detect and communicate disruptive incidents early and effectivel…