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Found 14 papers, 1 papers with code
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification
This paper contrasts the two canonical recurrent neural networks (RNNs) of long short-term memory (LSTM) and gated recurrent unit (GRU) to propose our novel light-weight RNN of Extrapolated Input for Network Simplification (EINS).
DecayNet: A Study on the Cell States of Long Short Term Memories
It is unclear whether the extensively applied long-short term memory (LSTM) is an optimised architecture for recurrent neural networks.
More About VLAD: A Leap From Euclidean to Riemannian Manifolds
This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds.
Bags of Affine Subspaces for Robust Object Tracking
We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames.
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks.
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
In particular, we search for a projection that yields a low-dimensional manifold with maximum discriminative power encoded via an affinity-weighted similarity measure based on metrics on the manifold.
Domain Adaptation on the Statistical Manifold
Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions.
K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection
For covariance-based image descriptors, taking into account the curvature of the corresponding feature space has been shown to improve discrimination performance.
Object Tracking via Non-Euclidean Geometry: A Grassmann Approach
A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream.
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold.
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry.
Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition
We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors.
Ranked #2 on
Hand Gesture Recognition
on Cambridge
Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
In contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance.
On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly
Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems.
