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Found 34 papers, 16 papers with code
Keyphrase Generation with GANs in Low-Resources Scenarios
Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document.
AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles
This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task.
A Language-based solution to enable Metaverse Retrieval
Recently, the Metaverse is becoming increasingly attractive, with millions of users accessing the many available virtual worlds.
On the Computation of the Gaussian Rate-Distortion-Perception Function
In this paper, we study the computation of the rate-distortion-perception function (RDPF) for a multivariate Gaussian source under mean squared error (MSE) distortion and, respectively, Kullback-Leibler divergence, geometric Jensen-Shannon divergence, squared Hellinger distance, and squared Wasserstein-2 distance perception metrics.
FArMARe: a Furniture-Aware Multi-task methodology for Recommending Apartments based on the user interests
Nowadays, many people frequently have to search for new accommodation options.
UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023
In this report, we present the technical details of our submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023.
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction
Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts.
Learning Sparsity of Representations with Discrete Latent Variables
The resulting sparsity of a representation is not fixed, but fits to the observation itself under the pre-defined restriction.
Generalizing over Long Tail Concepts for Medical Term Normalization
Medical term normalization consists in mapping a piece of text to a large number of output classes.
L2XGNN: Learning to Explain Graph Neural Networks
Graph Neural Networks (GNNs) are a popular class of machine learning models.
Automatic and effective discovery of quantum kernels
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.
AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task.
Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval
Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images.
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users.
UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022
This report presents the technical details of our submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022.
Ranked #3 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
Relevance-based Margin for Contrastively-trained Video Retrieval Models
We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance.
Ranked #7 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
Learning video retrieval models with relevance-aware online mining
Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention.
Ranked #5 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations.
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums.
BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection
Pretrained transformer-based models, such as BERT and its variants, have become a common choice to obtain state-of-the-art performances in NLP tasks.
Data augmentation techniques for the Video Question Answering task
Video Question Answering (VideoQA) is a task that requires a model to analyze and understand both the visual content given by the input video and the textual part given by the question, and the interaction between them in order to produce a meaningful answer.
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
Distilling the Evidence to Augment Fact Verification Models
We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim.
Text-to-Image Synthesis Based on Machine Generated Captions
To address this issue, in this paper we propose an approach capable of generating images starting from a given text using conditional GANs trained on uncaptioned images dataset.
Video-Based Convolutional Attention for Person Re-Identification
In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras.
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
Internet users generate content at unprecedented rates.
Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages
On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.
Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention
Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image representations, and Recurrent Neural Networks to generate the corresponding captions.
Ranked #2 on
Image Captioning
on Flickr30k Captions test
(using extra training data)
Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations.
A Deep Multi-Level Network for Saliency Prediction
Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps.
Video Registration in Egocentric Vision under Day and Night Illumination Changes
To effectively register an egocentric video sequence under these conditions, we propose to tackle the source of the problem: the matching process.
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing.
