Search Results for author: Radu Tudor Ionescu

Found 101 papers, 44 papers with code

A Report on the VarDial Evaluation Campaign 2020

no code implementations VarDial (COLING) 2020 Mihaela Gaman, Dirk Hovy, Radu Tudor Ionescu, Heidi Jauhiainen, Tommi Jauhiainen, Krister Lindén, Nikola Ljubešić, Niko Partanen, Christoph Purschke, Yves Scherrer, Marcos Zampieri

This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2020.

Dialect Identification

CBM: Curriculum by Masking

1 code implementation6 Jul 2024 Andrei Jarca, Florinel-Alin Croitoru, Radu Tudor Ionescu

We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum learning strategy that effectively creates an easy-to-hard training schedule via patch (token) masking, offering significant accuracy improvements over the conventional training regime and previous curriculum learning (CL) methods.

Object Recognition Transfer Learning

PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts

1 code implementation5 Jul 2024 Ana-Cristina Rogoz, Maria Ilinca Nechita, Radu Tudor Ionescu

We thus believe that PoPreRo is a valuable resource that can be used to evaluate models on predicting the popularity of social media posts in Romanian.

Language Modelling Large Language Model

PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction

1 code implementation7 Jun 2024 Eduard Poesina, Adriana Valentina Costache, Adrian-Gabriel Chifu, Josiane Mothe, Radu Tudor Ionescu

In order to determine the difficulty of the same prompts in image retrieval, we also collect manual annotations that represent retrieval performance.

Image Retrieval Information Retrieval +2

Curriculum Direct Preference Optimization for Diffusion and Consistency Models

no code implementations22 May 2024 Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Nicu Sebe, Mubarak Shah

Our approach, Curriculum DPO, is compared against state-of-the-art fine-tuning approaches on three benchmarks, outperforming the competing methods in terms of text alignment, aesthetics and human preference.

Text-to-Image Generation

A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus

1 code implementation20 May 2024 Eduard Poesina, Cornelia Caragea, Radu Tudor Ionescu

Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding.

Machine Translation Natural Language Inference +5

UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions

1 code implementation20 Apr 2024 Ana-Cristina Rogoz, Radu Tudor Ionescu

This work explores a novel data augmentation method based on Large Language Models (LLMs) for predicting item difficulty and response time of retired USMLE Multiple-Choice Questions (MCQs) in the BEA 2024 Shared Task.

Data Augmentation Multiple-choice

Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers

no code implementations14 Apr 2024 Diana-Nicoleta Grigore, Mariana-Iuliana Georgescu, Jon Alvarez Justo, Tor Johansen, Andreea Iuliana Ionescu, Radu Tudor Ionescu

Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources.

Knowledge Distillation

Cascaded Cross-Modal Transformer for Audio-Textual Classification

1 code implementation15 Jan 2024 Nicolae-Catalin Ristea, Andrei Anghel, Radu Tudor Ionescu

Subsequently, we combine language-specific Bidirectional Encoder Representations from Transformers (BERT) with Wav2Vec2. 0 audio features via a novel cascaded cross-modal transformer (CCMT).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles

1 code implementation10 Oct 2023 Daria-Mihaela Broscoteanu, Radu Tudor Ionescu

Despite the importance of the task, to the best of our knowledge, there is no publicly available clickbait corpus for the Romanian language.

Clickbait Detection Contrastive Learning

Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation

no code implementations29 Sep 2023 Vlad Hondru, Radu Tudor Ionescu

Our empirical results on two data sets confirm the superiority of our framework over two state-of-the-art methods in the few-call model extraction scenario.

Image Generation Knowledge Distillation +1

Learning Using Generated Privileged Information by Text-to-Image Diffusion Models

no code implementations26 Sep 2023 Rafael-Edy Menadil, Mariana-Iuliana Georgescu, Radu Tudor Ionescu

Learning Using Privileged Information is a particular type of knowledge distillation where the teacher model benefits from an additional data representation during training, called privileged information, improving the student model, which does not see the extra representation.

Knowledge Distillation text-classification +1

CL-MAE: Curriculum-Learned Masked Autoencoders

1 code implementation31 Aug 2023 Neelu Madan, Nicolae-Catalin Ristea, Kamal Nasrollahi, Thomas B. Moeslund, Radu Tudor Ionescu

In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task.

Representation Learning

JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition

no code implementations9 Aug 2023 Lucian Bicsi, Bogdan Alexe, Radu Tudor Ionescu, Marius Leordeanu

We propose JEDI, a multi-dataset semi-supervised learning method, which efficiently combines knowledge from multiple experts, learned on different datasets, to train and improve the performance of individual, per dataset, student models.

Action Recognition Temporal Action Localization

Reverse Stable Diffusion: What prompt was used to generate this image?

no code implementations2 Aug 2023 Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah

Our novel learning framework produces excellent results on the aforementioned task, yielding the highest gains when applied on the white-box model.

Text-to-Image Generation

Cascaded Cross-Modal Transformer for Request and Complaint Detection

no code implementations27 Jul 2023 Nicolae-Catalin Ristea, Radu Tudor Ionescu

We propose a novel cascaded cross-modal transformer (CCMT) that combines speech and text transcripts to detect customer requests and complaints in phone conversations.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Class Anchor Margin Loss for Content-Based Image Retrieval

no code implementations1 Jun 2023 Alexandru Ghita, Radu Tudor Ionescu

The performance of neural networks in content-based image retrieval (CBIR) is highly influenced by the chosen loss (objective) function.

Content-Based Image Retrieval Metric Learning +1

iQPP: A Benchmark for Image Query Performance Prediction

1 code implementation20 Feb 2023 Eduard Poesina, Radu Tudor Ionescu, Josiane Mothe

To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image.

Content-Based Image Retrieval Retrieval

FreCDo: A Large Corpus for French Cross-Domain Dialect Identification

1 code implementation15 Dec 2022 Mihaela Gaman, Adrian-Gabriel Chifu, William Domingues, Radu Tudor Ionescu

We present a novel corpus for French dialect identification comprising 413, 522 French text samples collected from public news websites in Belgium, Canada, France and Switzerland.

Dialect Identification

Audiovisual Masked Autoencoders

2 code implementations ICCV 2023 Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab

Can we leverage the audiovisual information already present in video to improve self-supervised representation learning?

 Ranked #1 on Audio Classification on EPIC-KITCHENS-100 (using extra training data)

Audio Classification Representation Learning

Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation

1 code implementation28 Nov 2022 Florinel-Alin Croitoru, Nicolae-Catalin Ristea, Dana Dascalescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah

We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models.

Anomaly Detection Knowledge Distillation +1

Diversity-Promoting Ensemble for Medical Image Segmentation

no code implementations22 Oct 2022 Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron

In this work, we propose a novel strategy to generate ensembles of different architectures for medical image segmentation, by leveraging the diversity (decorrelation) of the models forming the ensemble.

Diversity Image Segmentation +3

Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection

1 code implementation25 Sep 2022 Neelu Madan, Nicolae-Catalin Ristea, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah

In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.

Event Detection Fault Detection +1

Diffusion Models in Vision: A Survey

1 code implementation10 Sep 2022 Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah

Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling.

Denoising

VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web

no code implementations7 Jul 2022 Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu

The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.

Authorship Verification

Learning Rate Curriculum

1 code implementation18 May 2022 Florinel-Alin Croitoru, Nicolae-Catalin Ristea, Radu Tudor Ionescu, Nicu Sebe

In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-agnostic curriculum during the initial training epochs.

Audio Classification QNLI +2

Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution

1 code implementation8 Apr 2022 Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron, Olivian Savencu, Nicolae-Catalin Ristea, Nicolae Verga, Fahad Shahbaz Khan

Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention, and the number of convolutional filters controls the reduction rate of the channel attention, respectively.

Computed Tomography (CT) Image Super-Resolution

SepTr: Separable Transformer for Audio Spectrogram Processing

1 code implementation17 Mar 2022 Nicolae-Catalin Ristea, Radu Tudor Ionescu, Fahad Shahbaz Khan

Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community.

Audio Classification Speech Emotion Recognition +1

Discriminability-enforcing loss to improve representation learning

no code implementations14 Feb 2022 Florinel-Alin Croitoru, Diana-Nicoleta Grigore, Radu Tudor Ionescu

During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers.

Image Classification Representation Learning

Feature-level augmentation to improve robustness of deep neural networks to affine transformations

no code implementations10 Feb 2022 Adrian Sandru, Mariana-Iuliana Georgescu, Radu Tudor Ionescu

Recent studies revealed that convolutional neural networks do not generalize well to small image transformations, e. g. rotations by a few degrees or translations of a few pixels.

Data Augmentation Image Classification

A realistic approach to generate masked faces applied on two novel masked face recognition data sets

2 code implementations3 Sep 2021 Tudor Mare, Georgian Duta, Mariana-Iuliana Georgescu, Adrian Sandru, Bogdan Alexe, Marius Popescu, Radu Tudor Ionescu

We propose a method for enhancing data sets containing faces without masks by creating synthetic masks and overlaying them on faces in the original images.

Face Recognition

Contextual Convolutional Neural Networks

1 code implementation17 Aug 2021 Ionut Cosmin Duta, Mariana Iuliana Georgescu, Radu Tudor Ionescu

On the one hand, we integrate CoConv in the widely-used residual networks and show improved recognition performance over baselines on the core tasks and benchmarks for visual recognition, namely image classification on the ImageNet data set and object detection on the MS COCO data set.

Generative Adversarial Network Image Classification +3

Improving the Authentication with Built-in Camera Protocol Using Built-in Motion Sensors: A Deep Learning Solution

no code implementations22 Jul 2021 Cezara Benegui, Radu Tudor Ionescu

Our change to the ABC protocol results in a multi-modal protocol that lowers the false acceptance rate for the attack proposed in our previous work to a percentage as low as 0. 07%.

FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection

1 code implementation10 Apr 2021 Radu Tudor Ionescu, Adrian Gabriel Chifu

We employ two classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (average of CamemBERT word embeddings).

Satire Detection Unsupervised Domain Adaptation +1

Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set

no code implementations EACL 2021 Anca Tache, Gaman Mihaela, Radu Tudor Ionescu

Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools.

Clustering Sentiment Analysis +3

Self-paced ensemble learning for speech and audio classification

no code implementations22 Mar 2021 Nicolae-Catalin Ristea, Radu Tudor Ionescu

Instead of just combining the models, we propose a self-paced ensemble learning scheme in which models learn from each other over several iterations.

Audio Classification Ensemble Learning +2

Unsupervised Medical Image Alignment with Curriculum Learning

no code implementations20 Feb 2021 Mihail Burduja, Radu Tudor Ionescu

To the best of our knowledge, we are the first to attempt to improve performance by training medical image registration models using curriculum learning, starting from an easy training setup in the first training stages, and gradually increasing the complexity of the setup.

Image Registration Medical Image Registration

UnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning

no code implementations EACL (VarDial) 2021 Mihaela Gaman, Sebastian Cojocariu, Radu Tudor Ionescu

In this work, we describe our approach addressing the Social Media Variety Geolocation task featured in the 2021 VarDial Evaluation Campaign.

Dialect Identification Ensemble Learning +1

Curriculum Learning: A Survey

no code implementations25 Jan 2021 Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe

Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs.

BIG-bench Machine Learning Clustering

Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set

1 code implementation11 Jan 2021 Anca Maria Tache, Mihaela Gaman, Radu Tudor Ionescu

Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools.

Clustering Sentiment Analysis +3

Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

1 code implementation CVPR 2021 Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, Mubarak Shah

To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.

Abnormal Event Detection In Video Anomaly Detection In Surveillance Videos +4

Black-Box Ripper: Copying black-box models using generative evolutionary algorithms

1 code implementation NeurIPS 2020 Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu

To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box.

Evolutionary Algorithms

Combining Deep Learning and String Kernels for the Localization of Swiss German Tweets

no code implementations VarDial (COLING) 2020 Mihaela Gaman, Radu Tudor Ionescu

From simple models for regression, such as Support Vector Regression, to deep neural networks, such as Long Short-Term Memory networks and character-level convolutional neural networks, and, finally, to ensemble models based on meta-learners, such as XGBoost, our interest is focused on approaching the problem from a few different perspectives, in an attempt to minimize the prediction error.

Dialect Identification regression

SuPEr-SAM: Using the Supervision Signal from a Pose Estimator to Train a Spatial Attention Module for Personal Protective Equipment Recognition

no code implementations25 Sep 2020 Adrian Sandru, Georgian-Emilian Duta, Mariana-Iuliana Georgescu, Radu Tudor Ionescu

Typical approaches for PPE detection based on deep learning are (i) to train an object detector for items such as those listed above or (ii) to train a person detector and a classifier that takes the bounding boxes predicted by the detector and discriminates between people wearing and people not wearing the corresponding PPE items.

Adversarial Attacks on Deep Learning Systems for User Identification based on Motion Sensors

no code implementations2 Sep 2020 Cezara Benegui, Radu Tudor Ionescu

In this study, we focus on deep learning methods for explicit authentication based on motion sensor signals.

Face Recognition

To augment or not to augment? Data augmentation in user identification based on motion sensors

no code implementations1 Sep 2020 Cezara Benegui, Radu Tudor Ionescu

In order to prevent some of the possible attacks, these explicit authentication systems can be enhanced by considering a two-factor authentication scheme, in which the second factor is an implicit authentication system based on analyzing motion sensor data captured by accelerometers or gyroscopes.

BIG-bench Machine Learning Data Augmentation +1

Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks

1 code implementation11 Aug 2020 Nicolae-Cătălin Ristea, Andrei Anghel, Radu Tudor Ionescu

In order to train our network in a real-world scenario, we introduce a new data set of realistic automotive radar signals with multiple targets and multiple interferers.

Autonomous Driving

Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion

no code implementations3 Aug 2020 Mariana-Iuliana Georgescu, Radu Tudor Ionescu

First of all, we employ the classic teacher-student training strategy, in which the teacher is a CNN trained on fully-visible faces and the student is a CNN trained on occluded faces.

Facial Expression Recognition Facial Expression Recognition (FER) +1

The Unreasonable Effectiveness of Machine Learning in Moldavian versus Romanian Dialect Identification

1 code implementation30 Jul 2020 Mihaela Găman, Radu Tudor Ionescu

We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared to machine learning (ML) models.

BIG-bench Machine Learning Dialect Identification +2

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

1 code implementation21 Jul 2020 Nicolae-Cătălin Ristea, Andrei Anghel, Radu Tudor Ionescu

Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain.

Signal Processing

Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs

no code implementations17 Jun 2020 Nicolae-Cătălin Ristea, Radu Tudor Ionescu

Original and translated utterances are converted into spectrograms which are provided as input to a set of ResNet neural networks with various depths.

Data Augmentation

A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI

1 code implementation6 Jun 2020 Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu

In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model.

Non-linear Neurons with Human-like Apical Dendrite Activations

1 code implementation2 Feb 2020 Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Nicolae-Catalin Ristea, Nicu Sebe

In order to classify linearly non-separable data, neurons are typically organized into multi-layer neural networks that are equipped with at least one hidden layer.

Speech Emotion Recognition

Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

1 code implementation5 Jan 2020 Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Nicolae Verga

We evaluate our method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to relevant related works from the literature and baselines based on various interpolation schemes, using 2x and 4x scaling factors.

Image Super-Resolution

Convolutional Neural Networks for User Identificationbased on Motion Sensors Represented as Image

no code implementations8 Dec 2019 Cezara Benegui, Radu Tudor Ionescu

To pre-train the CNN and the RNN models for multi-class user classification, we use a different set of users than the set used for few-shot user identification, ensuring a realistic scenario.

General Classification

Forward and Backward Feature Selection for Query Performance Prediction

no code implementations4 Dec 2019 Sébastien Déjean, Radu Tudor Ionescu, Josiane Mothe, Md Zia Ullah

We found that: (1) our model based on a limited number of selected features is as good as more complex models for QPP and better than non-selective models; (2) our model is more efficient than complex models during inference time since it requires fewer features; (3) the predictive model is readable and understandable; and (4) one of our new QPP features is consistently selected across different collections, proving its usefulness.

feature selection Model Selection +1

Curriculum Self-Paced Learning for Cross-Domain Object Detection

no code implementations15 Nov 2019 Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe

To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e. g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN.

Domain Adaptation Object +2

Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks

no code implementations12 Nov 2019 Mariana-Iuliana Georgescu, Radu Tudor Ionescu

In this paper, we present an approach based on convolutional neural networks (CNNs) for facial expression recognition in a difficult setting with severe occlusions.

Facial Expression Recognition Facial Expression Recognition (FER)

Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)

1 code implementation20 Oct 2019 Petru Soviany, Claudiu Ardei, Radu Tudor Ionescu, Marius Leordeanu

All strategies are first based on ranking the training images by their difficulty scores, which are estimated by a state-of-the-art image difficulty predictor.

Image Generation Translation

A Report on the Third VarDial Evaluation Campaign

no code implementations WS 2019 Marcos Zampieri, Shervin Malmasi, Yves Scherrer, Tanja Samard{\v{z}}i{\'c}, Francis Tyers, Miikka Silfverberg, Natalia Klyueva, Tung-Le Pan, Chu-Ren Huang, Radu Tudor Ionescu, Andrei M. Butnaru, Tommi Jauhiainen

In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019.

Dialect Identification Morphological Analysis

Clustering Images by Unmasking - A New Baseline

no code implementations2 May 2019 Mariana-Iuliana Georgescu, Radu Tudor Ionescu

We propose a novel agglomerative clustering method based on unmasking, a technique that was previously used for authorship verification of text documents and for abnormal event detection in videos.

Authorship Verification Clustering +4

Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation

1 code implementation NAACL 2019 Radu Tudor Ionescu, Andrei M. Butnaru

The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword.

Multi-Label Text Classification Sentiment Analysis +3

MOROCO: The Moldavian and Romanian Dialectal Corpus

1 code implementation ACL 2019 Andrei M. Butnaru, Radu Tudor Ionescu

In this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at https://github. com/butnaruandrei/MOROCO.

Cultural Vocal Bursts Intensity Prediction

Continuous Trade-off Optimization between Fast and Accurate Deep Face Detectors

no code implementations27 Nov 2018 Petru Soviany, Radu Tudor Ionescu

All the approaches are based on separating the test images in two batches, an easy batch that is fed to a faster face detector and a difficult batch that is fed to a more accurate yet slower detector.

Face Detection

Transductive Learning with String Kernels for Cross-Domain Text Classification

no code implementations2 Nov 2018 Radu Tudor Ionescu, Andrei M. Butnaru

Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting.

Cross-Domain Text Classification General Classification +3

CocoNet: A deep neural network for mapping pixel coordinates to color values

1 code implementation29 May 2018 Paul Andrei Bricman, Radu Tudor Ionescu

It is important to note that we have to train an individual neural network for each input image, i. e. one network encodes a single image only.

Image Compression Image Denoising

UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row

no code implementations COLING 2018 Andrei M. Butnaru, Radu Tudor Ionescu

Furthermore, our top macro-F1 score (58. 92%) is significantly better than the second best score (57. 59%) in the 2018 ADI Shared Task, according to the statistical significance test performed by the organizers.

Dialect Identification

Local Learning with Deep and Handcrafted Features for Facial Expression Recognition

no code implementations29 Apr 2018 Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Marius Popescu

We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recognition.

Ranked #4 on Facial Expression Recognition (FER) on FER2013 (using extra training data)

Facial Expression Recognition Facial Expression Recognition (FER) +1

Real-Time Deep Learning Method for Abandoned Luggage Detection in Video

no code implementations3 Mar 2018 Sorina Smeureanu, Radu Tudor Ionescu

Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens.

Motion Estimation object-detection +1

Detecting abnormal events in video using Narrowed Normality Clusters

no code implementations12 Jan 2018 Radu Tudor Ionescu, Sorina Smeureanu, Marius Popescu, Bogdan Alexe

To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality.

Anomaly Detection Clustering +2

Can string kernels pass the test of time in Native Language Identification?

no code implementations WS 2017 Radu Tudor Ionescu, Marius Popescu

While most of our kernels are based on character p-grams (also known as n-grams) extracted from essays or speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided by the shared task organizers.

Native Language Identification

From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings

no code implementations25 Jul 2017 Andrei M. Butnaru, Radu Tudor Ionescu

In this paper, we propose a novel approach for text classification based on clustering word embeddings, inspired by the bag of visual words model, which is widely used in computer vision.

Clustering General Classification +3

Learning to Identify Arabic and German Dialects using Multiple Kernels

no code implementations WS 2017 Radu Tudor Ionescu, Andrei Butnaru

We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge.

Dialect Identification

UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels

no code implementations WS 2016 Radu Tudor Ionescu, Marius Popescu

Our approach is shallow and simple, but the empirical results obtained in the ADI Shared Task prove that it achieves very good results.

Dialect Identification Text Categorization

Cannot find the paper you are looking for? You can Submit a new open access paper.