Search Results for author:
Found 7 papers, 1 papers with code
Spiking Wavelet Transformer
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI
We propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM) which enables the automatic detection of cases of potential PMG.
Fully Associative Patch-based 1-to-N Matcher for Face Recognition
A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches.
A Hierarchical Matcher using Local Classifier Chains
This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture.
Patch-based Face Recognition using a Hierarchical Multi-label Matcher
Three ways are introduced to learn the global matching: majority voting, l1-regularized weighting, and decision rule.
A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes
This paper focuses on improving face recognition performance with a new signature combining implicit facial features with explicit soft facial attributes.
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.
