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Found 8 papers, 0 papers with code
FORML: A Riemannian Hessian-free Method for Meta-learning with Orthogonality Constraint
However, performing the optimization in the Riemannian space, where the parameters and meta-parameters are located on Riemannian manifolds is computationally intensive.
Occlusion Resilient 3D Human Pose Estimation
Occlusions remain one of the key challenges in 3D body pose estimation from single-camera video sequences.
Learning Deep Optimal Embeddings with Sinkhorn Divergences
Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data.
On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation
Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant.
3D Pose Estimation
Weakly-supervised 3D Human Pose Estimation
+1
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner.
Cross-Correlated Attention Networks for Person Re-Identification
Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -- to name a few -- when the task of person re-identification is considered.
Siamese Networks: The Tale of Two Manifolds
Siamese networks are non-linear deep models that have found their ways into a broad set of problems in learning theory, thanks to their embedding capabilities.
Geometry Aware Constrained Optimization Techniques for Deep Learning
In this paper, we extend some popular optimization algorithm to the Riemannian (constrained) setting.
