2 citations · 3 across the 21 of their papers we have counts for
8 papers · 1 filter
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…
A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods
Ioannis Kontostathis, Evlampios Apostolidis, Vasileios Mezaris
In this paper we introduce a new dataset for 360-degree video summarization: the transformation of 360-degree video content to concise 2D-video summaries that can be consumed via t…
An Integrated Framework for Multi-Granular Explanation of Video Summarization
Konstantinos Tsigos, Evlampios Apostolidis, Vasileios Mezaris
In this paper, we propose an integrated framework for multi-granular explanation of video summarization. This framework integrates methods for producing explanations both at the fr…
Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol
Konstantinos Apostolidis, Jakob Abesser, Luca Cuccovillo +1
This paper presents a baseline approach and an experimental protocol for a specific content verification problem: detecting discrepancies between the audio and video modalities in…
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
Konstantinos Tsigos, Evlampios Apostolidis, Spyridon Baxevanakis +2
In this paper we propose a new framework for evaluating the performance of explanation methods on the decisions of a deepfake detector. This framework assesses the ability of an ex…