Search Results for author:
Found 22 papers, 14 papers with code
Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol
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 multimedia content.
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
The findings of our quantitative and qualitative evaluations document the advanced performance of the LIME explanation method against the other compared ones, and indicate this method as the most appropriate for explaining the decisions of the utilized deepfake detector.
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
While some techniques for generating explanations have been proposed, primarily for Convolutional Neural Networks, adapting such techniques to the new paradigm of Vision Transformers is non-trivial.
Facilitating the Production of Well-tailored Video Summaries for Sharing on Social Media
This paper presents a web-based tool that facilitates the production of tailored summaries for online sharing on social media.
An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos
In this work, we present an integrated system for spatiotemporal summarization of 360-degrees videos.
Exploring Multi-Modal Fusion for Image Manipulation Detection and Localization
Recent image manipulation localization and detection techniques usually leverage forensic artifacts and traces that are produced by a noise-sensitive filter, such as SRM and Bayar convolution.
Ranked #1 on
Image Manipulation Detection
on DSO-1
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
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.
Masked Feature Modelling: Feature Masking for the Unsupervised Pre-training of a Graph Attention Network Block for Bottom-up Video Event Recognition
In this paper, we introduce Masked Feature Modelling (MFM), a novel approach for the unsupervised pre-training of a Graph Attention Network (GAT) block.
Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism
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 proposed.
TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks
TAME can easily be applied to any convolutional neural network (CNN) by streamlining the optimization of the attention mechanism's training method and the selection of target model's feature maps.
Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval
In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval.
Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism
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.
ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network
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 frame features, and a head network to process these features for the task of event recognition and explanation in video, is proposed.
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames
Instead of simply modeling the frames' dependencies based on global attention, our method integrates a concentrated attention mechanism that is able to focus on non-overlapping blocks in the main diagonal of the attention matrix, and to enrich the existing information by extracting and exploiting knowledge about the uniqueness and diversity of the associated frames of the video.
Ranked #1 on
Unsupervised Video Summarization
on TvSum
Combining Global and Local Attention with Positional Encoding for Video Summarization
This paper presents a new method for supervised video summarization.
Ranked #1 on
Video Summarization
on SumMe
Video Summarization Using Deep Neural Networks: A Survey
Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content.
AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video Summarization
This paper presents a new method for unsupervised video summarization.
Ranked #3 on
Unsupervised Video Summarization
on TvSum
Generative Adversarial Network
Unsupervised Video Summarization
Unsupervised Video Summarization via Attention-Driven Adversarial Learning
Experimental evaluation on two datasets (SumMe and TVSum) documents the contribution of the attention auto-encoder to faster and more stable training of the model, resulting in a significant performance improvement with respect to the original model and demonstrating the competitiveness of the proposed SUM-GAN-AAE against the state of the art.
Ranked #6 on
Unsupervised Video Summarization
on SumMe
A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization
In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization.
Ranked #5 on
Unsupervised Video Summarization
on TvSum
Learning to detect video events from zero or very few video examples
In this work we deal with the problem of high-level event detection in video.
Accelerated kernel discriminant analysis
In this paper, using a novel matrix factorization and simultaneous reduction to diagonal form approach (or in short simultaneous reduction approach), Accelerated Kernel Discriminant Analysis (AKDA) and Accelerated Kernel Subclass Discriminant Analysis (AKSDA) are proposed.
Linear Maximum Margin Classifier for Learning from Uncertain Data
In this paper, we propose a maximum margin classifier that deals with uncertainty in data input.
