Search Results for author:
Found 13 papers, 2 papers with code
Self-Supervised Learning for Time Series: Contrastive or Generative?
In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series.
Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification
As such, we merge the pre-trained Xception network with a multi-class variational classifier.
Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer
In order to develop a self monitoring mobile application, in this work, we propose a novel deep subspace analysis pipeline for semi-supervised diabetic foot ulcer mulit-label classification.
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning
These intermediate domains form a smooth path and bridge the gap between the source and target domains.
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors
In this paper, we show that without using any 3D information, KEPLER outperforms state of the art methods for alignment on challenging datasets such as AFW and AFLW.
Ranked #16 on
Head Pose Estimation
on BIWI
DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size
Large-scale supervised classification algorithms, especially those based on deep convolutional neural networks (DCNNs), require vast amounts of training data to achieve state-of-the-art performance.
Triplet Probabilistic Embedding for Face Verification and Clustering
Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.
Ranked #11 on
Face Verification
on IJB-A
Optimized Kernel-based Projection Space of Riemannian Manifolds
Recently, it was shown that embedding such manifolds into a Random Projection Spaces (RPS), rather than RKHS or tangent space, leads to higher classification and clustering performance.
Triplet Similarity Embedding for Face Verification
In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods.
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds.
Multi-Shot Person Re-Identification via Relational Stein Divergence
The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately.
Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance.
