217 citations · 447 across the 7 of their papers we have counts for
7 papers
Learning to Evaluate Performance of Multi-modal Semantic Localization
Zhiqiang Yuan, Wenkai Zhang, Chongyang Li +6
Semantic localization (SeLo) refers to the task of obtaining the most relevant locations in large-scale remote sensing (RS) images using semantic information such as text. As an em…
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
Zhiqiang Yuan, Wenkai Zhang, Kun Fu +4
Remote sensing (RS) cross-modal text-image retrieval has attracted extensive attention for its advantages of flexible input and efficient query. However, traditional methods ignore…
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
Zhiqiang Yuan, Wenkai Zhang, Changyuan Tian +5
Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on r…
FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute
Ao Wang, Shuai Chang, Huangshi Tian +5
Serverless computing, or Function-as-a-Service (FaaS), enables a new way of building and scaling applications by allowing users to deploy fine-grained functions while providing ful…
Oriented Objects as pairs of Middle Lines
Haoran Wei, Yue Zhang, Zhonghan Chang +3
The detection of oriented objects is frequently appeared in the field of natural scene text detection as well as object detection in aerial images. Traditional detectors for orient…
X-LineNet: Detecting Aircraft in Remote Sensing Images by a pair of Intersecting Line Segments
Haoran Wei, Yue Zhang, Bing Wang +3
Motivated by the development of deep convolution neural networks (DCNNs), tremendous progress has been gained in the field of aircraft detection. These DCNNs based detectors mainly…
A novel transfer learning method based on common space mapping and weighted domain matching
Ru-Ze Liang, Wei Xie, Weizhi Li +3
In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier…