Search Results for author:
Found 11 papers, 6 papers with code
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories.
StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
In this paper, we introduce StreakNet-Arch, a novel signal processing architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging systems, to address the limitations in scatter suppression and real-time imaging.
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Specifically, we introduce a concise multi-scale module to merge attentive features from quadruplet attention layers, and produces attribution maps.
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
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L3C-Stereo: Lossless Compression for Stereo Images
To tackle this, we propose L3C-Stereo, a multi-scale lossless compression model consisting of two main modules: the warping module and the probability estimation module.
Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization
Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering.
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.
Automated Measurements of Key Morphological Features of Human Embryos for IVF
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy.
A novel multimodal approach for hybrid brain-computer interface
It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals.
Human-Computer Interaction Signal Processing
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
