Unsupervised Video Summarization
16 papers with code • 2 benchmarks • 3 datasets
Unsupervised video summarization approaches overcome the need for ground-truth data (whose production requires time-demanding and laborious manual annotation procedures), based on learning mechanisms that require only an adequately large collection of original videos for their training. Specifically, the training is based on heuristic rules, like the sparsity, the representativeness, and the diversity of the utilized input features/characteristics.
TVSum
SumMe
OSTD
Latest papers
Enhancing Video Summarization with Context Awareness
Despite the importance of video summarization, there is a lack of diverse and representative datasets, hindering comprehensive evaluation and benchmarking of algorithms.
Cluster-based Video Summarization with Temporal Context Awareness
In this paper, we present TAC-SUM, a novel and efficient training-free approach for video summarization that addresses the limitations of existing cluster-based models by incorporating temporal context.
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score.
mehryar72/RS-SUM
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Conference 2023
We show that the reconstruction loss of the model for a video with masked frames correlates with the representativeness of the remaining frames in the video.
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score
mehryar72/RS-SUM
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Conference 2023
We show that the reconstruction loss of the model for a video with masked frames correlates with the representativeness of the remaining frames in the video.
Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization
Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing.
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames
e-apostolidis/CA-SUM
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ACM ICMR 2022
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.
ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN
This type of methods includes a summarizer and a discriminator.
Unsupervised Video Summarization via Multi-source Features
TIBHannover/UnsupervisedVideoSummarization
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Our evaluation shows that we obtain state-of-the-art results on both datasets, while also highlighting the shortcomings of previous work with regard to the evaluation methodology.
AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video Summarization
e-apostolidis/AC-SUM-GAN
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IEEE Transactions on Circuits and Systems for Video Technology 2020
This paper presents a new method for unsupervised video summarization.
Unsupervised Video Summarization via Attention-Driven Adversarial Learning
e-apostolidis/SUM-GAN-AAE
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MultiMedia Modeling (MMM) 2019
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.
