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Found 5 papers, 1 papers with code
ReDro: Efficiently Learning Large-sized SPD Visual Representation
When learning this representation in deep networks, eigen-decomposition of covariance matrix is usually needed for a key step called matrix normalisation.
Learning Compact Channel Correlation Representation for LiDAR Place Recognition
This paper presents a novel approach to learn compact channel correlation representation for LiDAR place recognition, called C3R, aimed at reducing the computational burden and dimensionality associated with traditional covariance pooling methods for place recognition tasks.
PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings
We propose PseudoNeg-MAE, a novel self-supervised learning framework that enhances global feature representation of point cloud mask autoencoder by making them both discriminative and sensitive to transformations.
Learning Partial Correlation based Deep Visual Representation for Image Classification
Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN.
Deep Learning based HEp-2 Image Classification: A Comprehensive Review
This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods.
