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Found 11 papers, 4 papers with code
Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing
In addition, to prevent the co-existence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new Known-class Meta Suppressor (KMS) module to suppress the activation of known-class objects in the sample.
RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework
It is combined by the Transformer module as a low-pass filter to extract global features of RS images through a dual-branch structure, and the CNN module as a stacked high-pass filter to extract fine-grained details effectively.
Learning to Evaluate Performance of Multi-modal Semantic Localization
Finally, we analyze the SeLo performance of RS cross-modal retrieval models in detail, explore the impact of different variables on this task, and provide a complete benchmark for the SeLo task.
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
Our model adapts to multi-scale feature inputs, favors multi-source retrieval methods, and can dynamically filter redundant features.
Ranked #8 on
Cross-Modal Retrieval
on RSITMD
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels.
Ranked #6 on
Cross-Modal Retrieval
on RSITMD
DMML-Net: Deep Metametric Learning for Few-Shot Geographic Object Segmentation in Remote Sensing Imagery
After metatraining, DMML-Net can be applied for the few-shot segmentation tasks of novel geographic objects with only a few gradient steps on the small training set.
AF-EMS Detector: Improve the Multi-Scale Detection Per- formance of the Anchor-Free Detector
With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection.
Ranked #14 on
Oriented Object Detection
on DOTA 1.0
Oriented Objects as pairs of Middle Lines
It is noteworthy that the objects in COCO can be regard as a special form of oriented objects with an angle of 90 degrees.
Ranked #13 on
Oriented Object Detection
on DOTA 1.0
X-LineNet: Detecting Aircraft in Remote Sensing Images by a pair of Intersecting Line Segments
Motivated by the development of deep convolution neural networks (DCNNs), tremendous progress has been gained in the field of aircraft detection.
A novel transfer learning method based on common space mapping and weighted domain matching
We map the data of two domains to one single common space, and learn a classifier in this common space.
Cross Domain Adaptation by Learning Partially Shared Classifiers and Weighting Source Data Points in the Shared Subspaces
In this paper, we propose a novel transfer learning method for this problem by learning a partially shared classifier for the target domain, and weighting the source domain data points.
