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Found 9 papers, 3 papers with code
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification
Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter selection.
Timer: Transformers for Time Series Analysis at Scale
Continuous progresses have been achieved as the emergence of large language models, exhibiting unprecedented ability in few-shot generalization, scalability, and task generality, which is however absent in time series models.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
While previous models suffer from complicated series variations induced by changing temporal distribution, we tackle non-stationary time series with modern Koopman theory that fundamentally considers the underlying time-variant dynamics.
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)
We compared the traditional data augmentation evaluation methods with our proposed cross-validation evaluation framework Results Using traditional data augmentation evaluation meta hods will give a false impression of improving the performance.
Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake
In this paper, we present a CNN-based Phase- Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets.
Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively.
