15 citations · 36 across the 9 of their papers we have counts for
9 papers
LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models
Nikolaos Gkalelis, Vasileios Mezaris
Large Vision-Language Models (VLMs) are successful in addressing a multitude of vision-language understanding tasks, such as Visual Question Answering (VQA), but their memory and c…
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…
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…
Accelerated kernel discriminant analysis
Nikolaos Gkalelis, Vasileios Mezaris
In this paper, using a novel matrix factorization and simultaneous reduction to diagonal form approach (or in short simultaneous reduction approach), Accelerated Kernel Discriminan…