Search Results for author:
Found 22 papers, 3 papers with code
Geometric-Aware Low-Light Image and Video Enhancement via Depth Guidance
First, a depth-aware feature extraction module is designed to inject depth priors into the image representation.
Video Frame Interpolation with Region-Distinguishable Priors from SAM
In existing Video Frame Interpolation (VFI) approaches, the motion estimation between neighboring frames plays a crucial role.
Large-scale data extraction from the UNOS organ donor documents
This dataset is by far larger than any previously available even in this preliminary phase; and 3) proving that our methods can be extended to all the past OPTN data and future data.
Vision HGNN: An Image is More than a Graph of Nodes
In this paper, we enhance ViG by transcending conventional "pairwise" linkages and harnessing the power of the hypergraph to encapsulate image information.
Search Behavior Prediction: A Hypergraph Perspective
With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}.
Learning Deep Optimal Embeddings with Sinkhorn Divergences
Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data.
Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays
Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers.
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata
In this paper, we proposed a novel and simple data augmentation method based on patient metadata and supervised knowledge to create clinically accurate positive and negative augmentations for chest X-rays.
Towards a Robust Differentiable Architecture Search under Label Noise
Our empirical evaluations show that the noise injecting operation does not degrade the performance of the NAS algorithm if the data is indeed clean.
CheXT: Knowledge-Guided Cross-Attention Transformer for Abnormality Classification and Localization in Chest X-rays
During training, the image branch leverages its learned attention to estimate pathology localization, which is then utilized to extract radiomic features from images in the radiomics branch.
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop
The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation.
Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning
Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era.
Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese
We report upon the results of a research and prototype building project \emph{Worldly~OCR} dedicated to developing new, more accurate image-to-text conversion software for several languages and writing systems.
Generating EEG features from Acoustic features
In this paper we demonstrate predicting electroencephalograpgy (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN).
Speech Synthesis using EEG
In this paper we demonstrate speech synthesis using different electroencephalography (EEG) feature sets recently introduced in [1].
Improving EEG based Continuous Speech Recognition
In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Voice Activity Detection in presence of background noise using EEG
In this paper we demonstrate that performance of voice activity detection (VAD) system operating in presence of background noise can be improved by concatenating acoustic input features with electroencephalography (EEG) features.
Sound Audio and Speech Processing Signal Processing
Spoken Speech Enhancement using EEG
In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model, gated recurrent unit (GRU) regression based model, temporal convolutional network (TCN) regression model and finally using a mixed TCN GRU regression model.
State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG
In this paper we first demonstrate continuous noisy speech recognition using electroencephalography (EEG) signals on English vocabulary using different types of state of the art end-to-end automatic speech recognition (ASR) models, we further provide results obtained using EEG data recorded under different experimental conditions.
Audio and Speech Processing Sound
Robust End-to-End Speaker Verification Using EEG
In this paper we demonstrate that performance of a speaker verification system can be improved by concatenating electroencephalography (EEG) signal features with speech signal features or only using EEG signal features.
Speech Recognition With No Speech Or With Noisy Speech Beyond English
In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limited joint English and Chinese vocabulary using EEG features with no speech signal as input.
