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Found 6 papers, 3 papers with code
Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion
Multimodal Named Entity Recognition (MNER) is a crucial task for information extraction from social media platforms such as Twitter.
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
On the one hand, RS-MetaNet raises the level of learning from the sample to the task by organizing training in a meta way, and it learns to learn a metric space that can well classify remote sensing scenes from a series of tasks.
DAPnet: A Double Self-attention Convolutional Network for Point Cloud Semantic Labeling
The incorporation of the double self-attention module has an average of 7\% improvement on the pre-class accuracy.
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
The feature-learning procedure of CNN largely depends on the architecture of CNN.
