Search Results for author:
Found 7 papers, 0 papers with code
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required.
Improving Visual Speech Enhancement Network by Learning Audio-visual Affinity with Multi-head Attention
The proposed model alleviates these drawbacks by a) applying a model that fuses audio and visual features layer by layer in encoding phase, and that feeds fused audio-visual features to each corresponding decoder layer, and more importantly, b) introducing a 2-stage multi-head cross attention (MHCA) mechanism to infer audio-visual speech enhancement for balancing the fused audio-visual features and eliminating irrelevant features.
GLD-Net: Improving Monaural Speech Enhancement by Learning Global and Local Dependency Features with GLD Block
For monaural speech enhancement, contextual information is important for accurate speech estimation.
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM
A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed.
VSEGAN: Visual Speech Enhancement Generative Adversarial Network
This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN).
Generative Model for Heterogeneous Inference
However, the learning and inference of BN model are NP-hard thus the number of stochastic variables in BN is highly constrained.
Using Deep Neural Network Approximate Bayesian Network
We present a new method to approximate posterior probabilities of Bayesian Network using Deep Neural Network.
