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Found 5 papers, 2 papers with code
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification.
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification
Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification.
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction.
Revisiting Flow Information for Traffic Prediction
However, existing traffic prediction methods focus on modeling complex spatiotemporal traffic correlations and seldomly study the influence of the original traffic flows among regions.
