62 citations · 350 across the 33 of their papers we have counts for
38 papers
ReferGPT: Towards Zero-Shot Referring Multi-Object Tracking
Tzoulio Chamiti, Leandro Di Bella, Adrian Munteanu +1
Tracking multiple objects based on textual queries is a challenging task that requires linking language understanding with object association across frames. Previous works typicall…
Traffic Event Detection as a Slot Filling Problem
Xiangyu Yang, Giannis Bekoulis, Nikos Deligiannis
In this paper, we introduce the new problem of extracting fine-grained traffic information from Twitter streams by also making publicly available the two (constructed) traffic-rela…
Learned Gradient Compression for Distributed Deep Learning
Lusine Abrahamyan, Yiming Chen, Giannis Bekoulis +1
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed tra…
Temporal Collaborative Filtering with Graph Convolutional Neural Networks
Esther Rodrigo Bonet, Duc Minh Nguyen, Nikos Deligiannis
Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around i…
A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation
Huynh Van Luong, Boris Joukovsky, Yonina C. Eldar +1
Deep unfolded neural networks are designed by unrolling the iterations of optimization algorithms. They can be shown to achieve faster convergence and higher accuracy than their op…
A Review on Fact Extraction and Verification
Giannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis
We study the fact checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its acc…
Interpretable Deep Multimodal Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Multimodal image super-resolution (SR) is the reconstruction of a high resolution image given a low-resolution observation with the aid of another image modality. While existing de…
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
Tien Huu Do, Duc Minh Nguyen, Giannis Bekoulis +2
Graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many metho…
Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted - Minimization: Architecture Design and Generalization Analysis
Huynh Van Luong, Boris Joukovsky, Nikos Deligiannis
Deep unfolding methods---for example, the learned iterative shrinkage thresholding algorithm (LISTA)---design deep neural networks as learned variations of optimization methods. Th…
Multimodal Deep Unfolding for Guided Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
The reconstruction of a high resolution image given a low resolution observation is an ill-posed inverse problem in imaging. Deep learning methods rely on training data to learn an…
DeepFPC: Deep Unfolding of a Fixed-Point Continuation Algorithm for Sparse Signal Recovery from Quantized Measurements
Peng Xiao, Bin Liao, Nikos Deligiannis
We present DeepFPC, a novel deep neural network designed by unfolding the iterations of the fixed-point continuation algorithm with one-sided l1-norm (FPC-l1), which has been propo…
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge abo…
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information
Evaggelia Tsiligianni, Nikos Deligiannis
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements. Typically, the recovery relies on complex numerical o…
Rumour Detection via News Propagation Dynamics and User Representation Learning
Tien Huu Do, Xiao Luo, Duc Minh Nguyen +1
Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours; it thus…
Designing recurrent neural networks by unfolding an l1-l1 minimization algorithm
Hung Duy Le, Huynh Van Luong, Nikos Deligiannis
We propose a new deep recurrent neural network (RNN) architecture for sequential signal reconstruction. Our network is designed by unfolding the iterations of the proximal gradient…
Geometric Matrix Completion with Deep Conditional Random Fields
Duc Minh Nguyen, Robert Calderbank, Nikos Deligiannis
The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially, recommender systems. Existing matrix comple…
Matrix Factorization via Deep Learning
Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix…
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +5
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial…
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
Duc Minh Nguyen, Evaggelia Tsiligianni, Robert Calderbank +1
Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Neverth…
Extendable Neural Matrix Completion
Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and r…
Twitter User Geolocation using Deep Multiview Learning
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +2
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows…
Online Decomposition of Compressive Streaming Data Using - Cluster-Weighted Minimization
Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer +1
We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves…
Multiview Deep Learning for Predicting Twitter Users' Location
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +2
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing.…
Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior
Nikos Deligiannis, João F. C. Mota, Evangelos Zimos +1
Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation,…