Search Results for author:
Found 32 papers, 7 papers with code
LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Perception System
We propose to update the edge model and its collaboration strategy with the cloud under the supervision of the large vision model, so as to adapt to the dynamic IoT data streams.
Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous Face Attribute Estimation
Face images contain a wide variety of attribute information.
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis
In this study, we aim to fill these gaps by conducting a comparative evaluation of state-of-the-art models in document layout analysis and investigating the potential of cross-lingual layout analysis by utilizing machine translation techniques.
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition
In this paper, we improve the frame-level classifier for word timings in E2E system by introducing label priors in connectionist temporal classification (CTC) loss, which is adopted from prior works, and combining low-level Mel-scale filter banks with high-level ASR encoder output as input feature.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sketch2Cloth: Sketch-based 3D Garment Generation with Unsigned Distance Fields
In this study, we propose Sketch2Cloth, a sketch-based 3D garment generation system using the unsigned distance fields from the user's sketch input.
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.
Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models.
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition
One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched.
Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization
The comparison of the proposed methods indicates that incorporating language information is more effective than disentangling for reducing language confusion in CS speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
An Online Sparse Streaming Feature Selection Algorithm
In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i. e., how to establish the uncertain relationship between sparse streaming features and labels.
Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition
More importantly, we train an end-to-end (E2E) speech recognition model by means of merging two monolingual data sets and observe the efficacy of the proposed ILME-based LM fusion for CSSR.
Intermediate-layer output Regularization for Attention-based Speech Recognition with Shared Decoder
Intermediate layer output (ILO) regularization by means of multitask training on encoder side has been shown to be an effective approach to yielding improved results on a wide range of end-to-end ASR frameworks.
Efficient Human-in-the-loop System for Guiding DNNs Attention
Attention guidance is an approach to addressing dataset bias in deep learning, where the model relies on incorrect features to make decisions.
Online Deep Learning from Doubly-Streaming Data
This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away.
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications.
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system.
Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based Encoder-decoder ASR
In this paper we propose two novel approaches to estimate the ILM based on Listen-Attend-Spell (LAS) framework.
Asymmetric 3D Context Fusion for Universal Lesion Detection
The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion.
Sketch-based Normal Map Generation with Geometric Sampling
This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling.
Improving RNN transducer with normalized jointer network
In this work, we analyze the cause of the huge gradient variance in RNN-T training and proposed a new \textit{normalized jointer network} to overcome it.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Dynamic latency speech recognition with asynchronous revision
In this work we propose an inference technique, asynchronous revision, to unify streaming and non-streaming speech recognition models.
Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
Fluorescein angiography can provide a map of retinal vascular structure and function, which is commonly used in ophthalmology diagnosis, however, this imaging modality may pose risks of harm to the patients.
AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness.
Domain adaptation model for retinopathy detection from cross-domain OCT images
A deep neural network (DNN) can assist in retinopathy screening by automatically classifying patients into normal and abnormal categories according to optical coherence tomography (OCT) images.
Reinventing 2D Convolutions for 3D Images
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.
Semi-supervised Skin Detection by Network with Mutual Guidance
In this paper we present a new data-driven method for robust skin detection from a single human portrait image.
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague.
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond
Blind image deblurring plays a very important role in many vision and multimedia applications.
Toward Designing Convergent Deep Operator Splitting Methods for Task-specific Nonconvex Optimization
Operator splitting methods have been successfully used in computational sciences, statistics, learning and vision areas to reduce complex problems into a series of simpler subproblems.
Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model
The system which combined frame retaining with frame stacking could reduces the time consumption of both training and decoding.
Deep LSTM for Large Vocabulary Continuous Speech Recognition
It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach.
Exponential Moving Average Model in Parallel Speech Recognition Training
As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently. We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model.
