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Found 9 papers, 4 papers with code
Cascaded Cross-Modal Transformer for Audio-Textual Classification
Subsequently, we combine language-specific Bidirectional Encoder Representations from Transformers (BERT) with Wav2Vec2. 0 audio features via a novel cascaded cross-modal transformer (CCMT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks
Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions.
Complex-Valued End-to-end Deep Network with Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification
A complex-valued (CV) end-to-end deep network is developed in this study for the reconstruction and classification of CV-SAR data.
Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar
The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context.
Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications.
Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery
Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.
Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks
In autonomous driving, radar systems play an important role in detecting targets such as other vehicles on the road.
Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks
In order to train our network in a real-world scenario, we introduce a new data set of realistic automotive radar signals with multiple targets and multiple interferers.
Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation
Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain.
Signal Processing
