4 citations · 6 across the 2 of their papers we have counts for
6 papers
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
Yingchao Feng, Wenhui Diao, Zhonghan Chang +3
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neura…
A Training-free, One-shot Detection Framework For Geospatial Objects In Remote Sensing Images
Tengfei Zhang, Yue Zhang, Xian Sun +3
Deep learning based object detection has achieved great success. However, these supervised learning methods are data-hungry and time-consuming. This restriction makes them unsuitab…
Comparison Network for One-Shot Conditional Object Detection
Tengfei Zhang, Yue Zhang, Xian Sun +4
The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads…
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Xue Yang, Jirui Yang, Junchi Yan +5
Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direc…
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Xue Yang, Hao Sun, Kun Fu +4
Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detec…
Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network
Xue Yang, Hao Sun, Xian Sun +3
Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the di…