Source-linked AI summary
Reconstructive Sequence-Graph Network for Video Summarization
Bin Zhao, Haopeng Li, Xiaoqiang Lu, Xuelong Li
TL;DR
Sequence-based video summarizers capture local dependencies but often miss long-distance relationships among shots. RSGN combines frame-level LSTM and shot-level graph encoding with reconstruction guidance; experiments report advantages over simpler variants on SumMe, TVsum, and VTW.
Problem
With huge volumes of online video, efficient browsing calls for automatic summarization; RNN-based approaches capture local dependencies but often fail to handle global long-distance ones.
Source: §1 · p. 1Method
RSGN encodes frames with an LSTM and shots as a graph, then uses a reconstructor to guide summary generation.
Source: §3.1 · p. 3, §3.2.2 · p. 3, §5 · p. 9Results
On SumMe and TVsum, hierarchical encoders outperform plain ones; on VTW, full RSGN surpasses simpler graph variants, while RSGNuns beats supervised baselines without a reconstructor on SumMe and TVsum.
Source: §4.2.1 · p. 6, §4.3 · p. 8Takeaways & Limitations
RSGN is designed to better preserve original video content and shot-level dependencies in summaries, with reconstruction guidance for the generator.
Source: §5 · p. 9
Abstract
from arXiv · showhide
Exploiting the inner-shot and inter-shot dependencies is essential for key-shot based video summarization. Current approaches mainly devote to modeling the video as a frame sequence by recurrent neural networks. However, one potential limitation of the sequence models is that they focus on capturing local neighborhood dependencies while the high-order dependencies in long distance are not fully exploited. In general, the frames in each shot record a certain activity and vary smoothly over time, but the multi-hop relationships occur frequently among shots. In this case, both the local and global dependencies are important for understanding the video content. Motivated by this point, we propose a Reconstructive Sequence-Graph Network (RSGN) to encode the frames and shots as sequence and graph hierarchically, where the frame-level dependencies are encoded by Long Short-Term Memory (LSTM), and the shot-level dependencies are captured by the Graph Convolutional Network (GCN). Then, the videos are summarized by exploiting both the local and global dependencies among shots. Besides, a reconstructor is developed to reward the summary generator, so that the generator can be optimized in an unsupervised manner, which can avert the lack of annotated data in video summarization. Furthermore, under the guidance of reconstruction loss, the predicted summary can better preserve the main video content and shot-level dependencies. Practically, the experimental results on three popular datasets i.e., SumMe, TVsum and VTW) have demonstrated the superiority of our proposed approach to the summarization task.
1 INTRODUCTION
Video summarization creates short, viewer-friendly skims, but sequence models may miss long-distance dependencies among shots. The paper proposes a hierarchical sequence-graph approach and a reconstructor to capture those dependencies and preserve video content.
1 INTRODUCTION
- Video skimming condenses a video into key shots to support viewer-friendly browsing and subsequent analysis.Source: §1 · p. 1
1.1 Motivation and Overview
- Sequence models capture local neighborhood dependencies but can miss global long-distance relationships and be distracted by noise.Source: §1 · p. 1, §1.1 · p. 2
- RSGN uses LSTM for within-shot temporal dependencies and a graph for pairwise dependencies among shots.Source: §1.1 · p. 2, §1.2 · p. 2
1.2 Contributions
- The reconstructor rewards the summary generator to preserve video content and key-shot dependencies while enabling unsupervised optimization.Source: §1.1 · p. 2, §1.2 · p. 2
- Across SumMe, TVsum, and VTW, the unsupervised version is comparable with supervised approaches, while the supervised version achieves state-of-the-art results in most cases.Source: §1.2 · p. 2
2 RELATED WORKS
Prior work includes unsupervised selection methods and supervised approaches that score or sequence summaries. RSGN is positioned as a hierarchical alternative that models shot-level global dependencies with a graph.
2.1 Unsupervised Video Summarization
- Unsupervised methods select representative frames or shots using clustering, including graph-based clustering, or use dictionary learning to reconstruct video content.Source: §2.1 · p. 2
2.2 Supervised Video Summarization
- Supervised approaches use summary property scores, hierarchical or recurrent sequence models, and adversarial or attention-based methods; RSGN instead models global shot dependencies with graph convolution.Source: §2.2 · pp. 2–3
3.1 Overview
RSGN combines a hierarchical summary generator with a graph-based reconstructor. The reconstructor guides training by assessing whether a candidate summary preserves the original video content and shot-level dependencies.
3.1 Overview
- The generator encodes frames within each shot using a bidirectional LSTM, then uses a graph model to capture dependencies among shots before selecting a summary candidate.Source: §3.1 · p. 3
- A summary-version graph encoder evaluates how well the candidate preserves video content and shot-level dependencies, rewarding the generator during reinforcement-based training.Source: §3.1 · p. 3
- The reconstructor supports both supervised and unsupervised training, but only the summary generator is used to summarize videos at test time.Source: §3.1 · p. 3
3.2 Sequence-Graph Summary Generator
The generator encodes each shot's frames as a sequence and models the shots as a graph. It combines shot features and graph relationships to estimate which shots to select.
3.2.1 Frame-level Sequence Encoder
- A bidirectional LSTM encodes each shot's frame sequence, and its final hidden state captures forward and backward temporal dependencies as the shot feature.Source: §3.2.1 · p. 3
3.2.2 Shot-level Graph Encoder
- The video is represented as a complete graph whose nodes are shots, so every pair of shots is connected to model their dependencies.Source: §3.2.2 · p. 3
- Edge weights represent shot dissimilarity, allowing graph aggregation to communicate information between shots with large semantic differences.Source: §3.2.2 · p. 3
- Graph convolution encodes relationships among all shots, and each shot's key-shot probability is determined jointly by its feature and its relation to the whole video.Source: §3.2.2 · p. 4
- Predicted probabilities drive Bernoulli sampling of summary shots; supervised training measures prediction error against human annotations using mean squared error.Source: §3.2.2 · p. 4
3.3 Graph Video Reconstructor
The graph video reconstructor evaluates whether a summary preserves video content and shot-level dependencies. It guides the generator toward key-shots that retain both.
3.3 Graph Video Reconstructor
- The reconstructor compares the original video graph with a summary-version graph to guide selection of key-shots that preserve video content and shot-level dependencies.The summary graph contains selected key-shots and their dependencies.Source: §3.3 · p. 4
3.3.1 Video Content Reconstruction
- Video content reconstruction encodes video shots according to their annotated importance scores and measures how reconstructive the summary content is.For unsupervised reconstruction, predicted probabilities replace the unavailable annotated scores.Source: §3.3.1 · p. 4
3.3.2 Shot-level Dependency Reconstruction
- Shot-level dependency reconstruction aims to retain storyline relationships, since severely damaging them can make the summary difficult to understand.The original graph encodes each shot’s dependencies, while the summary graph encodes dependencies among its key-shots.Source: §3.3.2 · p. 4
- The original-video and summary graphs have the same structure but different nodes, and use separate parameters because they represent different domains.The passage states that separate parameters are used in consideration of robustness and performance.Source: §3.3.2 · p. 4
3.4 Optimization
The generator and reconstructor are optimized end to end, with reconstruction rewards used in both supervised and unsupervised settings. The optimization also constrains summary size.
3.4 Optimization
- The generator and reconstructor are optimized end to end using reinforcement learning, with the reconstructor rewarding the summary generator.The expected reward is maximized, and the prediction loss is omitted in the unsupervised setting.Source: §3.4 · pp. 4–5
- Supervised optimization uses prediction loss alongside the reconstructor’s content and dependency rewards, while unsupervised optimization omits prediction loss.The prediction loss is denoted lp, and the reconstruction rewards are lc and ld.Source: §3.4 · pp. 4–5
- A regularization term constrains summary-candidate size because the reconstructor tends to reward selecting more key-shots.The distribution regularizer ε is fixed at 0.5 in this paper.Source: §3.4 · p. 5
- The episodic reinforcement algorithm optimizes the objective, with 10 episodes and 60 total training epochs.The hidden-state dimensionality is 256 for both the LSTM and graph; Adam uses learning rate and weight decay of 1e-5.Source: §3.4 · p. 5
4 EXPERIMENTS
Experiments evaluate RSGN on SumMe, TVsum, and VTW using F-measure and rank-based metrics. Across the three benchmarks, the unsupervised model is comparable with existing supervised approaches, while the supervised model reaches state-of-the-art results in most occasions.
4.1 Experimental Setup
- On SumMe and TVsum, experiments use 80/20 train-test splits repeated five times, average the results, and augment training data with OVP and YouTube videos.Source: §4.1 · p. 5
- VTW evaluation uses 2,529 of 18,100 videos annotated with shot-based highlights, transferred into frame-level importance scores.Source: §4.1 · p. 5
4.2.1 Results on SumMe and TVsum
- On SumMe and TVsum, hierarchical sequence-graph encoding and combined content/dependency reconstruction outperform simpler variants, while LSTM preserves local information.Source: Table 1 · p. 5, §4.2.1 · p. 6
- Augmented and transfer training settings improve results over canonical training for most approaches, and RSGNuns is comparable with supervised methods in most settings.Source: Table 5 · p. 7, §4.2.1 · p. 7
4.4 Rank-based Evaluation
- Because F-measure alone may not reflect summary quality comprehensively, rank-based evaluation finds supervised RSGN best on Kendall’s τ and Spearman’s ρ, with RSGNuns also competitive.Source: Table 6 · p. 8, §4.4 · p. 8
4.3 Results on VTW
- On VTW, the full hierarchical sequence-graph RSGN outperforms graph-only variants, and its unsupervised version is comparable with most supervised approaches.Source: Table 7 · p. 8, §4.3 · p. 8
5 CONCLUSIONS
RSGN addresses pure sequence models’ difficulty capturing global dependencies by hierarchically encoding frames as sequences and shots as a complete graph. A graph-based reconstructor guides the generator to preserve video content and shot-level dependencies, and results on three benchmarks support the approach.
5 CONCLUSIONS
- RSGN hierarchically encodes frame-level temporal dependencies with LSTM and inter-shot pairwise dependencies with GCN.The sequence-graph architecture is intended to avoid interference caused by the positional distance of shots.Source: §5 · p. 9
- A graph-based video reconstructor measures summary reconstructiveness and rewards the generator to preserve video content and shot-level dependencies.The reconstructor is a summary version of the graph encoder.Source: §5 · p. 9
- Experiments on three benchmarks verify the effectiveness of the sequence-graph summary generator and graph reconstructor.Source: §5 · p. 9