Papers › Combining Global and Local Attention with Positional Encoding for Video Summarization
Combining Global and Local Attention with Positional Encoding for Video Summarization
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections