Computer vision techniques are developed to enable computers to “see” and draw analysis from digital images or streaming videos.

Differences between SLU (Spoken Language Understanding) and NLU (Natural Language Understanding). Top FOSS and paid engines and their approach to SLU.

Explore the groundbreaking world of Generative AI, where machine learning meets human creativity.

Explore how hypergyroplanes and pseudo-gyrodistances revolutionize multinomial logistic regression in structure spaces, adapting it to the Riemannian manifold

Learn how hypergyroplanes and pseudo-gyrodistance redefine fully connected and convolutional layers for SPD neural networks

Explore the foundational layers and gyrovector space-inspired advancements in neural networks on SPD and Grassmann manifolds

Discover how advanced gyrovector space theories are driving innovations in deep neural networks on SPD and Grassmann manifolds.

Learn notations and algorithms for computing pseudo-gyrodistances, crucial for MLR in Riemannian manifold-based neural networks.

This paper presents novel advancements in SPD neural networks and Grassmann geometry for action recognition and node classification.

GyroSpd++ and Gr-GCN++ outperform baselines in human action recognition and node classification, showing superior accuracy on NTU60, FPHA, and Pubmed datasets.

Explore a novel method for Grassmann logarithmic maps and GCNs, improving graph embeddings and node learning on Grassmann manifolds

Gyrogroups, gyrocommutative gyrogroups, and gyrovector spaces are key to hyperbolic geometry and AI, offering insights into advanced mathematical structures

GyroSpd++ and Gr-GCN++ face limitations with hybrid methods and different Riemannian metrics, suggesting a need for optimization in SPD and Grassmann networks.

Gr-GCN++ outperforms competitors in node classification across Airport, Pubmed, and Cora datasets, highlighting the value of perspective in Grassmann manifolds

Evaluate GyroSpd++ for human action recognition using HDM05, FPHA, NTU60 datasets, with insights on performance, design, and comparisons to other methods

Detailed proofs for SPD spaces, inner products, pseudo-gyrodistances, and FC layers, referencing Nguyen & Yang (2023) and Pennec et al. (2020).

Learn to Compute canonical representations for SPD matrices using orthonormal matrices & SVD, enabling efficient geometric applications and matrix manipulation