Beast code in Giters

Evlampios Apostolidis's repositories

PGL-SUM

A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization" (IEEE ISM 2021)

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SUM-GAN-AAE

A PyTorch Implementation of SUM-GAN-AAE from "Unsupervised Video Summarization via Attention-Driven Adversarial Learning" (MMM 2020)

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AC-SUM-GAN

A PyTorch Implementation of AC-SUM-GAN from "AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video Summarization" (IEEE TCSVT 2021)

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CA-SUM

A PyTorch Implementation of CA-SUM from "Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames", Proc. ACM ICMR 2022

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Video-Thumbnail-Selector

A PyTorch Implementation of the Video Thumbnail Selector from "Combining Adversarial and Reinforcement Learning for Video Thumbnail Selection" (ACM ICMR 2021)

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A PyTorch implementation of the software used in: "A study on the use of attention for explaining video summarization" (NarSUM Workshop at ACM MM 2023) and "Explaining video summarization based on the focus of attention" (IEEE ISM 2022)

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PoR-Summarization-Measure

A python implementation for computing the PoR metric for video summarization from "Performance over Random: A Robust Evaluation Protocol for Video Summarization Methods" (ACM MM 2020)

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SUM-GAN-sl

A PyTorch Implementation of SUM-GAN-sl from "A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization" (AI4TV Workshop at ACM MM 2019)

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RL-DiVTS

A PyTorch Implementation of RL-DiVTS from "Selecting a Diverse Set of Aesthetically-Pleasing and Representative Video Thumbnails Using Reinforcement Learning" (ΙΕΕΕ ICIP 2023)

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360-VSumm

Dataset and scripts for paper "A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods", Proc. 1st Int. Workshop on Video for Immersive Experiences (Video4IMX-2024) at ACM IMX 2024, Stockholm, Sweden, June 2024.

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CA-SUM-360

A PyTorch implementation of our method from "An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos", Proc. MMM 2024