Search Results for author:
Found 12 papers, 2 papers with code
Implicit Neural Representations for Variable Length Human Motion Generation
We confirm that our method with a Transformer decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC datasets in terms of realism and diversity of generated motions.
Multimodal Emotion Recognition with High-level Speech and Text Features
Current works train deep learning models on low-level data representations to solve the emotion recognition task.
MSR-DARTS: Minimum Stable Rank of Differentiable Architecture Search
To overcome this problem, we propose a method called minimum stable rank DARTS (MSR-DARTS), for finding a model with the best generalization error by replacing architecture optimization with the selection process using the minimum stable rank criterion.
Ranked #24 on
Neural Architecture Search
on CIFAR-10
Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network
We propose a non-invasive and cost-effective method to automatically detect dementia by utilizing solely speech audio data.
MaskConvNet: Training Efficient ConvNets from Scratch via Budget-constrained Filter Pruning
(4) Fast, it is observed that the number of training epochs required by MaskConvNet is close to training a baseline without pruning.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition
We investigate the feasibility of sequence-level knowledge distillation of Sequence-to-Sequence (Seq2Seq) models for Large Vocabulary Continuous Speech Recognition (LVSCR).
Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances
With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect such systems to understand complicated social scenes, including those outdoors, in cafeterias, and hospitals.
Few-Shot Adaptation for Multimedia Semantic Indexing
Few-shot adaptation provides robust parameter estimation with few training examples, by optimizing the parameters of zero-shot learning and supervised many-shot learning simultaneously.
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
This paper presents a new framework for human action recognition from a 3D skeleton sequence.
Ranked #94 on
Skeleton Based Action Recognition
on NTU RGB+D
I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification
I-vector based text-independent speaker verification (SV) systems often have poor performance with short utterances, as the biased phonetic distribution in a short utterance makes the extracted i-vector unreliable.
