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Found 9 papers, 6 papers with code
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.
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.
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
