no code implementations • 26 Dec 2023 • Yingqi Lin, Xiaogang Xu, Yan Han, Jiafei Wu, Zhe Liu
First, a depth-aware feature extraction module is designed to inject depth priors into the image representation.
no code implementations • 26 Dec 2023 • Yan Han, Xiaogang Xu, Yingqi Lin, Jiafei Wu, Zhe Liu
In existing Video Frame Interpolation (VFI) approaches, the motion estimation between neighboring frames plays a crucial role.
no code implementations • 30 Aug 2023 • Marek Rychlik, Bekir Tanriover, Yan Han
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.
1 code implementation • ICCV 2023 • Yan Han, Peihao Wang, Souvik Kundu, Ying Ding, Zhangyang Wang
In this paper, we enhance ViG by transcending conventional "pairwise" linkages and harnessing the power of the hypergraph to encapsulate image information.
1 code implementation • 23 Nov 2022 • Yan Han, Edward W Huang, Wenqing Zheng, Nikhil Rao, Zhangyang Wang, Karthik Subbian
With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}.
no code implementations • 14 Sep 2022 • Soumava Kumar Roy, Yan Han, Mehrtash Harandi, Lars Petersson
Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data.
1 code implementation • 10 Jul 2022 • Yan Han, Gregory Holste, Ying Ding, Ahmed Tewfik, Yifan Peng, Zhangyang Wang
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.
no code implementations • 27 Oct 2021 • Ajay Jaiswal, TianHao Li, Cyprian Zander, Yan Han, Justin F. Rousseau, Yifan Peng, Ying Ding
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.
no code implementations • 23 Oct 2021 • Christian Simon, Piotr Koniusz, Lars Petersson, Yan Han, Mehrtash Harandi
Our empirical evaluations show that the noise injecting operation does not degrade the performance of the NAS algorithm if the data is indeed clean.
no code implementations • 29 Sep 2021 • Yan Han, Ying Ding, Ahmed Tewfik, Yifan Peng, Zhangyang Wang
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.
no code implementations • 11 Apr 2021 • Yan Han, Chongyan Chen, Ahmed Tewfik, Benjamin Glicksberg, Ying Ding, Yifan Peng, Zhangyang Wang
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.
no code implementations • 12 Jan 2021 • Yan Han, Chongyan Chen, Ahmed H Tewfik, Ying Ding, Yifan Peng
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.
no code implementations • 25 Nov 2020 • Yan Han, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, Yifan Peng
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
no code implementations • 8 May 2020 • Marek Rychlik, Dwight Nwaigwe, Yan Han, Dylan Murphy
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.
no code implementations • 29 Feb 2020 • Gautam Krishna, Co Tran, Mason Carnahan, Yan Han, Ahmed H. Tewfik
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).
no code implementations • 22 Feb 2020 • Gautam Krishna, Co Tran, Yan Han, Mason Carnahan
In this paper we demonstrate speech synthesis using different electroencephalography (EEG) feature sets recently introduced in [1].
no code implementations • 24 Nov 2019 • Gautam Krishna, Co Tran, Mason Carnahan, Yan Han, Ahmed H. Tewfik
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) +3
no code implementations • 8 Nov 2019 • Gautam Krishna, Co Tran, Yan Han, Mason Carnahan, Ahmed H. Tewfik
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
no code implementations • 13 Sep 2019 • Gautam Krishna, Co Tran, Yan Han, Mason Carnahan, Ahmed H. Tewfik
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.
no code implementations • 14 Aug 2019 • Gautam Krishna, Yan Han, Co Tran, Mason Carnahan, Ahmed H. Tewfik
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
no code implementations • 17 Jun 2019 • Yan Han, Gautam Krishna, Co Tran, Mason Carnahan, Ahmed H. Tewfik
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.
no code implementations • 17 Jun 2019 • Gautam Krishna, Co Tran, Yan Han, Mason Carnahan, Ahmed H. Tewfik
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.