6 citations · 11 across the 10 of their papers we have counts for
12 papers
Improving Multimodal Hateful Meme Detection Exploiting LMM-Generated Knowledge
Maria Tzelepi, Vasileios Mezaris
Memes have become a dominant form of communication in social media in recent years. Memes are typically humorous and harmless, however there are also memes that promote hate speech…
LMM-Regularized CLIP Embeddings for Image Classification
Maria Tzelepi, Vasileios Mezaris
In this paper we deal with image classification tasks using the powerful CLIP vision-language model. Our goal is to advance the classification performance using the CLIP's image en…
Disturbing Image Detection Using LMM-Elicited Emotion Embeddings
Maria Tzelepi, Vasileios Mezaris
In this paper we deal with the task of Disturbing Image Detection (DID), exploiting knowledge encoded in Large Multimodal Models (LMMs). Specifically, we propose to exploit LMM kno…
Online Anchor-based Training for Image Classification Tasks
Maria Tzelepi, Vasileios Mezaris
In this paper, we aim to improve the performance of a deep learning model towards image classification tasks, proposing a novel anchor-based training methodology, named \textit{Onl…
Exploiting LMM-based knowledge for image classification tasks
Maria Tzelepi, Vasileios Mezaris
In this paper we address image classification tasks leveraging knowledge encoded in Large Multimodal Models (LMMs). More specifically, we use the MiniGPT-4 model to extract semanti…
Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management
Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos +10
Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchang…
Deep Learning for Energy Time-Series Analysis and Forecasting
Maria Tzelepi, Charalampos Symeonidis, Paraskevi Nousi +5
Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in t…
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
N. Passalis, S. Pedrazzi, R. Babuska +15
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation
Maria Tzelepi, Anastasios Tefas
Knowledge Distillation has been established as a highly promising approach for training compact and faster models by transferring knowledge from heavyweight and powerful models. Ho…
Quadratic mutual information regularization in real-time deep CNN models
Maria Tzelepi, Anastasios Tefas
In this paper, regularized lightweight deep convolutional neural network models, capable of effectively operating in real-time on devices with restricted computational power for hi…
Semantic Scene Segmentation for Robotics Applications
Maria Tzelepi, Anastasios Tefas
Semantic scene segmentation plays a critical role in a wide range of robotics applications, e.g., autonomous navigation. These applications are accompanied by specific computationa…
Heterogeneous Knowledge Distillation using Information Flow Modeling
Nikolaos Passalis, Maria Tzelepi, Anastasios Tefas
Knowledge Distillation (KD) methods are capable of transferring the knowledge encoded in a large and complex teacher into a smaller and faster student. Early methods were usually l…