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Found 12 papers, 1 papers with code
Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes
Thanks to the application of deep learning technology in point cloud processing of the remote sensing field, point cloud segmentation has become a research hotspot in recent years, which can be applied to real-world 3D, smart cities, and other fields.
SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing
To solve the above problem, we re-examine the deformable learning method in the Multi-View Stereo task and propose a novel paradigm based on view Space and Depth deformable Learning (SDL-MVS), aiming to learn deformable interactions of features in different view spaces and deformably model the depth ranges and intervals to enable high accurate depth estimation.
Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond
Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information.
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.
DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation
Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing.
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).
Learning Regularized Positional Encoding for Molecular Prediction
To model the complex nonlinearity in predicting molecular properties in an more end-to-end approach, we propose to encode the positional quantities with a learnable embedding that is continuous and differentiable.
Supervised Pretraining for Molecular Force Fields and Properties Prediction
Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks.
Fooling Adversarial Training with Inducing Noise
Adversarial training is widely believed to be a reliable approach to improve model robustness against adversarial attack.
GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction
In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms.
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.
Fooling Adversarial Training with Induction Noise
Adversarial training is widely believed to be a reliable approach to improve model robustness against adversarial attack.
