237 citations · 304 across the 12 of their papers we have counts for
12 papers
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Qihang Yu, Ju He, Xueqing Deng +2
Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories. One way to address this challenge is to leverage mul…
GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research
Dalton Lunga, Yingjie Hu, Shawn Newsam +4
Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many o…
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization
Xueqing Deng, Dawei Sun, Shawn Newsam +1
Recent Knowledge distillation (KD) studies show that different manually designed schemes impact the learned results significantly. Yet, in KD, automatically searching an optimal di…
NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night
Xueqing Deng, Peng Wang, Xiaochen Lian +1
The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared…
AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation
Xueqing Deng, Yi Zhu, Yuxin Tian +1
Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case. However, such data i…
Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery
Xueqing Deng, Yi Zhu, Yuxin Tian +1
Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully conne…
Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
Xueqing Deng, Yi Zhu, Shawn Newsam
This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network…
Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval
Weixun Zhou, Xueqing Deng, Zhenfeng Shao
Conventional remote sensing image retrieval (RSIR) systems usually perform single-label retrieval where each image is annotated by a single label representing the most significant…
What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks
Xueqing Deng, Yi Zhu, Shawn Newsam
This paper investigates conditional generative adversarial networks (cGANs) to overcome a fundamental limitation of using geotagged media for geographic discovery, namely its spars…
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images
Yi Zhu, Xueqing Deng, Shawn Newsam
We perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not p…
Spatial Morphing Kernel Regression For Feature Interpolation
Xueqing Deng, Yi Zhu, Shawn Newsam
In recent years, geotagged social media has become popular as a novel source for geographic knowledge discovery. Ground-level images and videos provide a different perspective than…
Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use
Xueqing Deng, Shawn Newsam
This paper performs a quantitative comparison of open-source data available on the Internet for the fine-grain mapping of land use. Three points of interest (POI) data sources--Goo…