research
∙
04/10/2023
SOOD: Towards Semi-Supervised Oriented Object Detection
Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled dat...
share
research
∙
03/09/2023
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection
In this paper, we present a simple yet effective semi-supervised 3D obje...
research
∙
10/31/2022
Model Compression for DNN-Based Text-Independent Speaker Verification Using Weight Quantization
DNN-based models achieve high performance in the speaker verification (S...
research
∙
10/31/2022
Convolution-Based Channel-Frequency Attention for Text-Independent Speaker Verification
Deep convolutional neural networks (CNNs) have been applied to extractin...
research
∙
10/17/2022
Private Data Valuation and Fair Payment in Data Marketplaces
Data valuation is an essential task in a data marketplace. It aims at fa...
research
∙
08/22/2022
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Causal inference has become a powerful tool to handle the out-of-distrib...
research
∙
07/07/2022
What Makes for Automatic Reconstruction of Pulmonary Segments
3D reconstruction of pulmonary segments plays an important role in surgi...
research
∙
06/26/2022
Transport-Oriented Feature Aggregation for Speaker Embedding Learning
Pooling is needed to aggregate frame-level features into utterance-level...
research
∙
05/25/2022
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech
The performance of child speech recognition is generally less satisfacto...
research
∙
09/20/2021
Improving Text-Independent Speaker Verification with Auxiliary Speakers Using Graph
The paper presents a novel approach to refining similarity scores betwee...
research
∙
06/16/2021
Detection of Consonant Errors in Disordered Speech Based on Consonant-vowel Segment Embedding
Speech sound disorder (SSD) refers to a type of developmental disorder i...
research
∙
08/16/2019
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
Group convolution, which divides the channels of ConvNets into groups, h...
research
∙
08/16/2019
Differentiable Learning-to-Group Channels viaGroupable Convolutional Neural Networks
Group convolution, which divides the channels of ConvNets into groups, h...
research
∙
07/22/2019
Switchable Normalization for Learning-to-Normalize Deep Representation
We address a learning-to-normalize problem by proposing Switchable Norma...
research
∙
03/09/2019
SSN: Learning Sparse Switchable Normalization via SparsestMax
Normalization methods improve both optimization and generalization of Co...
research
∙
07/16/2018