47 citations · 112 across the 15 of their papers we have counts for
11 papers · 1 filter
P-TAME: Explain Any Image Classifier with Trained Perturbations
Mariano V. Ntrougkas, Vasileios Mezaris, Ioannis Patras
The adoption of Deep Neural Networks (DNNs) in critical fields where predictions need to be accompanied by justifications is hindered by their inherent black-box nature. In this pa…
VidCtx: Context-aware Video Question Answering with Image Models
Andreas Goulas, Vasileios Mezaris, Ioannis Patras
To address computational and memory limitations of Large Multimodal Models in the Video Question-Answering task, several recent methods extract textual representations per frame (e…
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
Mariano V. Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
The development and adoption of Vision Transformers and other deep-learning architectures for image classification tasks has been rapid. However, the "black box" nature of neural n…
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
Konstantinos Gkrispanis, Nikolaos Gkalelis, Vasileios Mezaris
Face detectors are becoming a crucial component of many applications, including surveillance, that often have to run on edge devices with limited processing power and memory. There…
Masked Feature Modelling: Feature Masking for the Unsupervised Pre-training of a Graph Attention Network Block for Bottom-up Video Event Recognition
Dimitrios Daskalakis, Nikolaos Gkalelis, Vasileios Mezaris
In this paper, we introduce Masked Feature Modelling (MFM), a novel approach for the unsupervised pre-training of a Graph Attention Network (GAT) block. MFM utilizes a pretrained V…
Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism
Nikolaos Gkalelis, Dimitrios Daskalakis, Vasileios Mezaris
In this paper, Gated-ViGAT, an efficient approach for video event recognition, utilizing bottom-up (object) information, a new frame sampling policy and a gating mechanism is propo…
TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks
Mariano Ntrougkas, Nikolaos Gkalelis, Vasileios Mezaris
The apparent ``black box'' nature of neural networks is a barrier to adoption in applications where explainability is essential. This paper presents TAME (Trainable Attention Mecha…
Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval
Damianos Galanopoulos, Vasileios Mezaris
In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple divers…
Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism
Ioanna Gkartzonika, Nikolaos Gkalelis, Vasileios Mezaris
In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural network (DCNN) image classifiers, called L-CAM-Fm and L-CAM-Img, are proposed. Both…
ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network
Nikolaos Gkalelis, Dimitrios Daskalakis, Vasileios Mezaris
In this paper a pure-attention bottom-up approach, called ViGAT, that utilizes an object detector together with a Vision Transformer (ViT) backbone network to derive object and fra…
Video Summarization Using Deep Neural Networks: A Survey
Evlampios Apostolidis, Eleni Adamantidou, Alexandros I. Metsai +2
Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed…