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Found 50 papers, 9 papers with code
Large Language Models: A Survey
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022.
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning
In addition, our model can extract visual information as suggested by the text prompt, e. g., "an object in image one is moving northeast", and generate corresponding videos.
Modern Augmented Reality: Applications, Trends, and Future Directions
Augmented reality (AR) is one of the relatively old, yet trending areas in the intersection of computer vision and computer graphics with numerous applications in several areas, from gaming and entertainment, to education and healthcare.
Going Deeper Into Face Detection: A Survey
In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks.
Age and Gender Prediction From Face Images Using Attentional Convolutional Network
We train our model in a multi-task learning fashion, and augment the feature embedding of the age classifier, with the predicted gender, and show that doing so can further increase the accuracy of age prediction.
COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network
We also provide a visualization of the attention maps of the model for several test images, and show that our model is attending to the infected regions as intended.
Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder
We used universal sentence encoder in order to derive the semantic representation and the similarity of tweets.
COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net
Through experimental results on a relatively large-scale CT segmentation dataset of around 900 images, we show that adding this new regularization term leads to 2\% gain on overall segmentation performance compared to the U-Net model.
Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
In this work, we propose a model based on sentence Transformer to detect the main topics of Tweets in recent months.
Deep Learning Based Text Classification: A Comprehensive Review
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference.
Palm-GAN: Generating Realistic Palmprint Images Using Total-Variation Regularized GAN
We apply this framework to a popular palmprint databases, and generate images which look very realistic, and similar to the samples in this database.
Regularized Submodular Maximization at Scale
In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$.
Image Segmentation Using Deep Learning: A Survey
Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others.
Biometrics Recognition Using Deep Learning: A Survey
Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years.
Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision
We propose a neural network architecture for learning vector representations of hotels.
Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection
Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition and accomplishes such a task when the background is stationary and the foreground is dynamic and relatively small.
FingerNet: Pushing The Limits of Fingerprint Recognition Using Convolutional Neural Network
Fingerprint recognition has been utilized for cellphone authentication, airport security and beyond.
DeepIris: Iris Recognition Using A Deep Learning Approach
Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control.
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models
With the popularity of social networks, and e-commerce websites, sentiment analysis has become a more active area of research in the past few years.
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
In recent years, several works proposed an end-to-end framework for facial expression recognition, using deep learning models.
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Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN
Through experimental results, we show that the generated fingerprint images have a good diversity, and are able to capture different parts of the prior distribution.
Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN
Generating iris images which look realistic is both an interesting and challenging problem.
Efficient Super Resolution For Large-Scale Images Using Attentional GAN
At Expedia Group, we were tasked with generating images of at least 2000px for display on the website, four times greater than the sizes typically reported in the literature.
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features
Unlike most of previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task.
Ad-Net: Audio-Visual Convolutional Neural Network for Advertisement Detection In Videos
Personalized advertisement is a crucial task for many of the online businesses and video broadcasters.
Image Segmentation Using Subspace Representation and Sparse Decomposition
In this dissertation, we focus on the extraction of text and graphics in mixed-content images, and design novel approaches for various aspects of this problem.
A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI
Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1. 7 million people annually in US.
Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words
Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US.
A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling
While diffusion MRI has been extremely promising in the study of MTBI, identifying patients with recent MTBI remains a challenge.
Automatic Question-Answering Using A Deep Similarity Neural Network
We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data.
Text Extraction From Texture Images Using Masked Signal Decomposition
Text extraction is an important problem in image processing with applications from optical character recognition to autonomous driving.
An ADMM Approach to Masked Signal Decomposition Using Subspace Representation
Signal decomposition is a classical problem in signal processing, which aims to separate an observed signal into two or more components each with its own property.
Subspace Learning in The Presence of Sparse Structured Outliers and Noise
Subspace learning is an important problem, which has many applications in image and video processing.
An Experimental Study of Deep Convolutional Features For Iris Recognition
Different features have been used to perform iris recognition in the past.
Image Segmentation Using Overlapping Group Sparsity
Sparse decomposition has been widely used for different applications, such as source separation, image classification and image denoising.
Image Decomposition Using a Robust Regression Approach
This paper considers how to separate text and/or graphics from smooth background in screen content and mixed content images and proposes an algorithm to perform this segmentation task.
Face Recognition Using Scattering Convolutional Network
Face recognition has been an active research area in the past few decades.
Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition
This paper considers how to separate text and/or graphics from smooth background in screen content and mixed document images and proposes two approaches to perform this segmentation task.
Palmprint Recognition Using Deep Scattering Convolutional Network
Many algorithms have been proposed for palmprint recognition in the past, majority of them being based on features extracted from the transform domain.
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization
Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more.
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
In this paper, sparse decomposition is used for image segmentation.
Fingerprint Recognition Using Translation Invariant Scattering Network
Different features and algorithms have been used for fingerprint recognition in the past.
Iris Recognition Using Scattering Transform and Textural Features
Iris recognition has drawn a lot of attention since the mid-twentieth century.
Screen Content Image Segmentation Using Least Absolute Deviation Fitting
The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity and cannot be modeled by this smooth representation.
A Robust Regression Approach for Background/Foreground Segmentation
Background/foreground segmentation has a lot of applications in image and video processing.
On The Power of Joint Wavelet-DCT Features for Multispectral Palmprint Recognition
Biometric-based identification has drawn a lot of attention in the recent years.
Multispectral Palmprint Recognition Using Textural Features
In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data.
Highly Accurate Multispectral Palmprint Recognition Using Statistical and Wavelet Features
Palmprint is one of the most useful physiological biometrics that can be used as a powerful means in personal recognition systems.
Multispectral Palmprint Recognition Using a Hybrid Feature
Personal identification problem has been a major field of research in recent years.
A Geometric Approach For Fully Automatic Chromosome Segmentation
The next step is detection of touching and overlapping chromosomes, and the final step is separation of such chromosomes.
