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Found 13 papers, 2 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.
OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images
One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head.
SiamTHN: Siamese Target Highlight Network for Visual Tracking
The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region.
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery
Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction).
Beyond the Limitation of Monocular 3D Detector via Knowledge Distillation
Moreover, we construct a depth-guided matrix by the predicted depth gap of teacher and student to facilitate the model to learn more knowledge of farther objects in prediction level distillation.
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection
Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress.
Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification
With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds.
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders
Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misclassification of instances with similar spatial structures.
Double Similarity Distillation for Semantic Image Segmentation
In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively.
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15, 000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M.
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
Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images
Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding.
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network.
