Papers › Combining Global and Local Attention with Positional Encoding for Video Summarization

Combining Global and Local Attention with Positional Encoding for Video Summarization

1 Dec 2021IEEE International Symposium on Multimedia (ISM) 2021 12archive 2025-07-28

Evlampios Apostolidis, Georgios Balaouras, Vasileios Mezaris, Ioannis Patras

This paper presents a new method for supervised video summarization. To overcome drawbacks of existing RNN-based summarization architectures, that relate to the modeling of long-range frames' dependencies and the ability to parallelize the training process, the developed model relies on the use of self-attention mechanisms to estimate the importance of video frames. Contrary to previous attention-based summarization approaches that model the frames' dependencies by observing the entire frame sequence, our method combines global and local multi-head attention mechanisms to discover different modelings of the frames' dependencies at different levels of granularity. Moreover, the utilized attention mechanisms integrate a component that encodes the temporal position of video frames - this is of major importance when producing a video summary. Experiments on two datasets (SumMe and TVSum) demonstrate the effectiveness of the proposed model compared to existing attention-based methods, and its competitiveness against other state-of-the-art supervised summarization approaches. An ablation study that focuses on our main proposed components, namely the use of global and local multi-head attention mechanisms in collaboration with an absolute positional encoding component, shows their relative contributions to the overall summarization performance.

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Code

e-apostolidis/PGL-SUM officialmentioned in paperpytorch report

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Tasks

Supervised Video SummarizationVideo Summarization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Supervised Video Summarization SumMe PGL-SUM (maximum learning capacity) F1-score (Canonical) 57.1 #1 of 21 Archive leaderboard report
Supervised Video Summarization SumMe PGL-SUM F1-score (Canonical) 55.6 #2 of 21 Archive leaderboard report
Supervised Video Summarization TvSum PGL-SUM (maximum learning capacity) F1-score (Canonical) 62.7 #8 of 21 Archive leaderboard report
Supervised Video Summarization TvSum PGL-SUM F1-score (Canonical) 61.0 #12 of 21 Archive leaderboard report
Supervised Video Summarization TvSum PGL-SUM Kendall's Tau 0.157 #12 of 21 Archive leaderboard report
Supervised Video Summarization TvSum PGL-SUM Spearman's Rho 0.206 #12 of 21 Archive leaderboard report
Video Summarization SumMe PGL-SUM F1-score (Canonical) 55.6 #1 of 6 Archive leaderboard report
Video Summarization TvSum PGL-SUM F1-score (Canonical) 61.0 #5 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionDropoutEarly StoppingGradient ClippingLayer NormalizationLinear LayerMulti-Head AttentionReLURelative Position EncodingsResidual ConnectionSigmoid ActivationSoftmaxWeight DecayXavier Initialization

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