Source-linked AI summary
Summarizing Videos with Attention
Jiri Fajtl, Hajar Sadeghi Sokeh, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino
TL;DR
Video summarization requires selecting informative keyshots despite difficult contextual criteria and subjective annotations. The paper introduces a recurrent-free soft self-attention network that performs sequence-to-sequence transformation with fully connected regression, and reports state-of-the-art performance on TvSum and SumMe.
Problem
Handcrafted feature distances and clustering criteria struggle to capture contextual information and subjective human judgments in video summarization.
Method
VASNet replaces recurrent encoder-decoder networks with soft self-attention and fully connected regression, processing variable-length sequences in a single forward/backward pass.
Results
The model outperforms existing state-of-the-art methods on the TvSum and SumMe supervised keyshot benchmarks.
Takeaways & Limitations
Its simpler architecture is easier to implement and less resource demanding than LSTM encoder-decoder methods, supporting embedded or low-power applications.
Takeaways & Limitations
Global attention can assign variance-increasing weight patterns on long videos because it explores temporally distant frames at every prediction step.
Abstract
from arXiv · showhide
In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mechanism. Current state of the art methods leverage bi-directional recurrent networks such as BiLSTM combined with attention. These networks are complex to implement and computationally demanding compared to fully connected networks. To that end we propose a simple, self-attention based network for video summarization which performs the entire sequence to sequence transformation in a single feed forward pass and single backward pass during training. Our method sets a new state of the art results on two benchmarks TvSum and SumMe, commonly used in this domain.
1 Introduction
Video summarization is difficult because handcrafted similarity criteria do not capture contextual information or subjective human judgments. The paper therefore proposes replacing recurrent encoder-decoder models with a simpler soft self-attention architecture for supervised keyshot summarization.
- 1 Introduction: Video summarization must select keyshots that preserve important information, but representativeness and diversity are difficult to define from visual features alone.The paper focuses on keyshot-based summaries composed of shorter video sequences.
- 1 Introduction: 15-20 user annotations per video in TvSum and SumMe yield pairwise F-score ∼0.34, revealing substantial subjectivity in video summarization.These annotation differences motivate supervised learning rather than a manually designed clustering metric.
- 1 Introduction: Current state-of-the-art systems commonly use computationally demanding bi-directional LSTM or GRU encoder-decoder architectures with soft attention.The recurrent design supports sequence-to-sequence summarization but is complex and expensive compared with fully connected networks.
- 1 Introduction: Each prediction uses self-attention weights to compute a weighted average of input features, which a fully connected network regresses into a frame importance score.This describes the model’s central attention and score-regression operations.
- 1 Introduction: VASNet proposes soft self-attention for sequence-to-sequence video summarization, replacing recurrent LSTM/GRU encoder-decoder methods.The contribution is framed as a simpler recurrent-network replacement based on attention.
2 Related Work
Related work spans recurrent supervised summarization, adversarial and reinforcement-learning approaches, semantic side information, clustering methods, and neural attention mechanisms. The paper situates its method within soft self-attention, which enables differentiable end-to-end sequence processing without decoder-state conditioning.
- 2 Related Work: Supervised summarization methods have used LSTM-based temporal modeling, determinantal point processes, bi-directional LSTM context encoding, and sequential generative adversarial networks.These approaches target representative, compact, or distribution-matched summaries through different sequence modeling strategies.
- 2 Related Work: Other methods use reinforcement learning or reward functions combining summary diversity and representativeness, including approaches for unsupervised summarization.The related work includes hierarchical and end-to-end learning designs.
- 2 Related Work: Some systems incorporate side semantic information such as titles, queries, descriptions, comments, or unpaired summary data alongside visual features.These methods extend visual representations with surrounding textual or auxiliary information.
- 2 Related Work: Unsupervised clustering approaches select keyframes by removing similar frames or deriving weights from frame-similarity graphs and deep video features.Their selection process is based on feature similarity and clustering structure.
- 2.1 Attention Techniques: Soft attention assigns differentiable probability-like weights, allowing backpropagation and end-to-end training, whereas hard attention uses stochastic binary selection.The paper exclusively focuses on soft attention.
- 2.1 Attention Techniques: Self-attention omits decoder-state conditioning and instead models relations among input samples with respect to the optimization objective.This mechanism has been used for summarization and task-independent sequence representations.
3 Model Architecture
VASNet replaces recurrent encoder-decoder architectures with soft self-attention and fully connected regression to predict frame importance scores. The scores are converted into keyshots using temporal segmentation and length-constrained selection.
- Attention: Unlike recurrent models, the attention aperture directly accesses past and future inputs and can be asymmetric, dilated, or exclude the current step.This provides non-causal access without requiring a bidirectional recurrent network.
- Architecture: VASNet replaces LSTM/GRU encoder-decoder networks with soft self-attention and a fully connected frame-score regressor.The architecture processes CNN feature vectors for video frames and outputs a frame-level score sequence of the same length.
- Attention: Self-attention assigns each input feature a probability weight relative to the desired frame-level score at time t.The attention weights are computed from alignment scores and normalized with softmax.
- Score regression: Weighted transformed inputs are averaged into a context vector, which is projected and combined residually before frame-score regression.The regression network uses ReLU and sigmoid layers with dropout and layer normalization.
- Keyshot generation: Predicted frame scores are converted into keyshots by detecting scene changes with KTS and selecting shots with Knapsack under a 15% summary-length constraint.Selected keyshots are concatenated into the final summary and represented as a binary frame vector for evaluation.
- Computational considerations: Self-attention has complexity O(N^2D), while recurrent processing has O(N) sequential operations of complexity O(ND^2); local attention is suggested for longer videos.Self-attention is computationally preferable when sequence length N is shorter than feature size D.
4 Evaluation
Evaluation compares supervised keyshot summarization across TvSum and SumMe, using established datasets, preprocessing, cross-validation, and F-score protocols. The machine summaries are evaluated against user summaries through frame-level overlap and dataset-specific aggregation.
- Datasets: Experiments use TvSum and SumMe for direct comparison, while OVP and YouTube augment training because only TvSum and SumMe have suitable keyshot labels.OVP and YouTube are converted to frame-level scores and binary keyshot summaries when used.
- Datasets: TvSum provides frame-level importance scores, whereas SumMe provides binary keyshot summaries.OVP and YouTube instead provide keyframe annotations requiring conversion.
- Experimental settings: Five-fold cross-validation uses 80% of each dataset for training and 20% for testing in canonical and augmented settings.The augmented setting adds other datasets to the training split.
- Evaluation protocol: The evaluation follows prior protocols and measures similarity between machine and user summaries using the harmonic mean of precision and recall as percentage F-score.True positives, false positives, and false negatives are computed per frame from overlap between ground-truth and machine summaries.
- Evaluation protocol: Machine summaries are limited to 15% of the original video length before evaluation against multiple user summaries.TvSum averages scores against each user summary, whereas SumMe selects the most similar user summary per video.
5 Experiments and Results
VASNet outperforms previous methods on TvSum and SumMe in canonical and augmented settings, while qualitative analyses show alignment with ground-truth importance and scene transitions.
- Quantitative Results: VASNet outperforms all previous methods in both canonical and augmented settings on TvSum and SumMe.Table 3 compares the method with state-of-the-art systems and human performance.
- Quantitative Results: 12% and 11% are the improvements over prior work on SumMe in canonical and augmented settings, respectively.VASNet is 21% below human performance on SumMe.
- Quantitative Results: The higher SumMe gain may reflect greater information extraction from ground truth, whereas TvSum methods already approach human performance.The authors also suggest that global attention may negatively affect long TvSum sequences.
- Limitations: Global attention examines all video frames at every prediction step, potentially increasing attention-weight variance for long sequences.The authors hypothesize that local attention could mitigate this effect.
- Qualitative Results: Predicted scores correlate with ground truth, selected keyshots align with most ground-truth peaks, and attention-weight transitions correlate with scene changes.These qualitative results are illustrated for TvSum test videos and video 7.
6 Conclusions
The proposed keyshot summarization network uses pure soft self-attention without recurrent encoder-decoder networks, outperforming existing methods on TvSum and SumMe. Its simple architecture is easier to implement, less resource demanding, and intended for embedded or low-power platforms.
- The model performs keyshot video summarization with pure soft self-attention and no recurrent networks such as LSTM encoder-decoders.
- The model outperforms existing state-of-the-art methods on the supervised keyshot summarization benchmarks TvSum and SumMe.
- Its simplicity makes the model easier to implement and less resource demanding than LSTM encoder-decoder methods, supporting embedded or low-power applications.
- The architecture uses one global self-attention layer followed by two fully connected layers, without positional encoding.
- The authors state that complete training and evaluation source code, together with trained weights, will be publicly available for reproducibility.