Source-linked AI summary
Video Summarization with Attention-Based Encoder-Decoder Networks
Zhong Ji, Kailin Xiong, Yanwei Pang, Xuelong Li
TL;DR
Video summarization seeks compact keyshot sequences from original frame sequences while learning how humans select important content. The paper proposes AVS, an attentive encoder-decoder with bidirectional LSTM encoding and additive or multiplicative attention-based LSTM decoding. AVS-based models outperform competing methods on two benchmark datasets by 0.8%-3%.
Problem
Prior encoder-decoder summarization models compress all necessary information into one fixed-length context vector, limiting explicit frame weighting and risking lost temporal structure.
Source: §I · p. 1Method
AVS uses a bidirectional LSTM encoder and attention-based LSTM decoders with additive and multiplicative score functions to generate frame importance scores for keyshot selection.
Source: §I · pp. 1–2, A. Encoder with Bidirectional LSTM… · p. 4, B. Decoder with Attention Mechanism · p. 5Results
0.8%-3%: the proposed AVS models outperform competing methods on two benchmark datasets.
Source: §V · p. 7Takeaways & Limitations
AVS provides a supervised attentive framework that uses human guidance to model complex attention mechanisms for video summarization.
Source: C. Attention-Based Video Summarization… · p. 3, §V · p. 7
Abstract
from arXiv · showhide
This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named Attentive encoder-decoder networks for Video Summarization (AVS), in which the encoder uses a Bidirectional Long Short-Term Memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on three video summarization benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches,with remarkable improvements from 0.8% to 3% on two datasets,respectively..
I. INTRODUCTION
The paper motivates supervised video summarization as a way to make increasingly large video collections easier to browse, focusing on keyshot-based video skims. It proposes AVS, an attentive encoder-decoder framework that learns frame importance and generates keyshots.
- Video summarization produces compact yet comprehensive summaries for efficient browsing, indexing, retrieval, and event detection.Source: §I · p. 1
- The work focuses on video skims, which represent summaries as representative video segments called keyshots.Source: §I · p. 1
- Supervised approaches learn summarization capability from human labels, while prior encoder-decoder methods model video summarization as sequence-to-sequence learning.Source: §I · p. 1
- A key drawback of prior encoder-decoder models is compressing the entire input into one fixed-length context vector, preventing explicit frame-specific weighting and risking lost temporal structure.Source: §I · p. 1
- AVS conditions decoder generation on encoder hidden states and uses attention to assign different importance weights to input frames.Source: §I · pp. 1–2
- The AVS framework combines an encoder-decoder model, which generates importance scores, with a keyshot selection model that produces keyshots.Source: Figure 1 · p. 2, §I · p. 2
II. RELATED WORK
Related work spans unsupervised and supervised summarization, single- and multi-video settings, and recurrent approaches for modeling temporal dependencies. Prior RNN-based methods include vsLSTM, dppLSTM, and SUM-GAN variants.
- Single-video summarization digests one long video, whereas multi-video summarization summarizes many short query-based web videos; this work focuses on single-video summarization.Source: §II · p. 2
- Video summarization methods include unsupervised criteria-driven approaches and supervised methods trained with human-labeled summaries.Source: §II · p. 2, A. Unsupervised and Supervised Video… · p. 2
- Supervised methods have modeled representativeness, relevance, uniformity, diversity, importance, and storyness through structured or submodular objectives.Source: A. Unsupervised and Supervised Video… · p. 2
- RNN-based video summarization includes vsLSTM, which uses bidirectional LSTM for variable-range dependencies, and dppLSTM, which adds DPP for diversity.Source: B. RNN-Based Video Summarization… · p. 3
- SUM-GAN uses an autoencoder LSTM generator to reconstruct videos from selected frames, with an LSTM discriminator distinguishing original and reconstructed videos.Source: B. RNN-Based Video Summarization… · p. 3
- Related recurrent approaches also address video highlights, defined as moments of major or special interest, using recurrent autoencoders with LSTM cells.Source: B. RNN-Based Video Summarization… · p. 3
C. Attention-Based Video Summarization Approaches
Earlier attention-based summarization methods derive importance from low-level visual cues, but their unsupervised design cannot use human guidance. AVS instead learns attention supervision within a deep framework for selecting keyshots.
- User attention treats highly attended video segments as more important and more likely to become keyshots.Source: C. Attention-Based Video Summarization… · p. 3
- Existing attention-based methods combine cues such as motion, static appearance, faces, camera attention, audio saliency, image signatures, and temporal gradients.Source: C. Attention-Based Video Summarization… · p. 3
- Low-level attention cues may not adequately reflect user interest, and multiple features do not guarantee correlation with what users find interesting.Source: C. Attention-Based Video Summarization… · p. 3
- Because prior attention-based approaches are unsupervised, they cannot exploit human guidance, whereas AVS learns the attention mechanism supervisedly.Source: C. Attention-Based Video Summarization… · p. 3
- AVS formulates summarization as sequence-to-sequence learning, measures frame importance with an encoder-decoder, and converts frame scores into budgeted shot-level summaries.Source: §III · p. 3
- The framework introduces a bidirectional LSTM encoder to capture contextual information needed for deciding which frames or shots belong in a summary.Source: §III · p. 3
A. Encoder with Bidirectional LSTM Network
The AVS framework encodes video-frame context with a BiLSTM and uses decoder attention to weight encoder outputs when generating summary scores. It explores additive and multiplicative score functions for relating input frames to decoder states.
A. Encoder with Bidirectional LSTM Network
- A BiLSTM encodes each frame using both preceding and following frames.Forward and backward LSTMs process the sequence in opposite directions, and their hidden states are concatenated into an annotation for each frame.Source: A. Encoder with Bidirectional LSTM… · p. 4
B. Decoder with Attention Mechanism
- Attention replaces the fixed-length context vector with selective weighting of encoder outputs during decoding.The decoder can focus on a subset of input frames by increasing their attention weights at each time step.Source: B. Decoder with Attention Mechanism · p. 4
- Each relevance score combines an encoder output with the decoder’s previous hidden state before normalization into attention weights.The weighted encoder outputs provide the decoder input used to generate the output sequence.Source: Figure 3 · p. 4, B. Decoder with Attention Mechanism · p. 4
- AVS develops A-AVS and M-AVS using additive and multiplicative score functions, respectively.The multiplicative model is introduced to better use the relationship between matched visual-frame features and decoder hidden states.Source: B. Decoder with Attention Mechanism · p. 5
C. Keyshots Selection
After predicting frame importance, AVS segments coherent frames into shots, averages frame scores within each shot, and selects keyshots under a length constraint.
C. Keyshots Selection
- Predicted frame importance scores are converted into shot-level scores using Kernel Temporal Segmentation.KTS segments visually coherent frames into shots, whose importance is the average importance of their constituent frames.Source: C. Keyshots Selection · p. 5
- Keyshot selection is formulated as a 0/1 knapsack problem over shot importance and shot length.Dynamic programming solves the optimization, and selected shots are concatenated chronologically to form the summary.Source: C. Keyshots Selection · p. 5
- The resulting summary concatenates the selected shots in chronological order.Shots with nonzero selection indicators are included after optimization.Source: C. Keyshots Selection · p. 5
IV. EXPERIMENTS AND ANALYSIS
The experiments section covers datasets, evaluation metrics, experimental settings, main results, parameter analysis, data augmentation, and qualitative results.
IV. EXPERIMENTS AND ANALYSIS
- The section introduces datasets, evaluation metrics, experimental settings, main results, parameter analysis, data augmentation, and qualitative results.These components define the paper’s experimental analysis plan.Source: §IV · p. 5
1) Datasets:
The study evaluates AVS on the SumMe and TVSum video summarization benchmarks using F-measure based on temporal overlap between generated and ground-truth summaries.
Datasets
- SumMe contains 25 raw videos covering varied events, while TVSum contains 50 edited YouTube videos across 10 categories.Most videos in both datasets are 1 to 10 minutes long.Source: 1) Datasets · p. 5
Evaluation Metrics
- The methods generate summaries shorter than 15% of the original video duration.Source: 2) Evaluation Metrics · p. 5
- Precision and recall are computed for each generated-summary and ground-truth pair using their temporal overlaps.Source: 2) Evaluation Metrics · p. 5
- F-measure combines precision and recall as the evaluation metric.Source: 2) Evaluation Metrics · p. 5
3) Experimental Settings:
The experiments use standardized visual features and supervised train/test splits, comparing AVS with unsupervised and supervised state-of-the-art summarization methods.
Experimental Settings
- Videos are downsampled to 2 fps, with 1024-dimensional GoogLeNet pool5 features extracted for each frame.Source: 3) Experimental Settings · p. 6
- Both AVS models contain three LSTM layers with 256 units per layer, and the decoder attention scale is set to 9.Source: 3) Experimental Settings · p. 6
- Training and testing use disjoint portions of the same dataset, with 20% of the data randomly reserved for testing.Source: 3) Experimental Settings · p. 6
Compared Methods
- The comparison includes eight state-of-the-art approaches spanning unsupervised and supervised video summarization methods.Source: 1) Comparison with State-of-the-art… · p. 6
Results
- Both A-AVS and M-AVS outperform all competitors on both datasets, with gains of at least 3 absolute points on TVSum and nearly 1 point on SumMe.Source: 1) Comparison with State-of-the-art… · p. 6
- M-AVS performs about 0.5%-1.6% better than A-AVS, while A-AVS exceeds SUM-GANdpp by 0.8% on SumMe and 3.1% on TVSum.Source: 1) Comparison with State-of-the-art… · p. 6
2) Importance Evaluation of Attention Mechanism:
Additional experiments examine attention removal, attention-scale sensitivity, and training-data augmentation, supporting the effectiveness and parameter dependence of the AVS framework.
Importance Evaluation of Attention Mechanism
- Removing attention creates the LSTM-VS baseline, which AVS outperforms by 6%-10%.Source: 2) Importance Evaluation of Attention… · pp. 6–7
Parameter Sensitive Analysis
- A-AVS performance peaks when the attention scale is around 9 on SumMe and TVSum.Source: 3) Parameter Sensitive Analysis · p. 7, Figure 6 · p. 7
- The authors relate the best attention scale to the average shot length of approximately 9 frames after KTS segmentation.Source: 3) Parameter Sensitive Analysis · p. 7
Augmentation Experiments
- Augmenting training data with annotated videos from other datasets improves summarization performance.Source: C. Augmentation Experiments · p. 7
- With augmentation, both proposed methods rise about 0.7% on SumMe, while TVSum gains are 1.4% for A-AVS and 0.8% for M-AVS.Source: C. Augmentation Experiments · p. 7
D. Qualitative Results
On a TVSum example, A-AVS and M-AVS select more high-importance shots and distribute selected frames more uniformly over time than vsLSTM. The paper also reports 0.8%-3% improvements over competing methods on two benchmark datasets, while noting that limited dataset scale constrains further development.
Qualitative comparison
- A-AVS and M-AVS select more shots with larger ground-truth importance scores than the compared methods on a TVSum example.The compared models are vsLSTM, LSTM-VS, A-AVS, and M-AVS.Source: D. Qualitative Results · p. 7
- The proposed methods produce summaries with a more uniform temporal distribution than vsLSTM.Source: D. Qualitative Results · p. 7
Benchmark outcome
- 0.8%-3% improvements over competing methods are reported on two benchmark datasets.Source: §V · p. 7
Future work and limitation
- Insufficient training data restricts the performance and development of supervised video summarization approaches.The authors identify existing datasets as insufficiently large and propose transfer learning and GAN techniques as future directions.Source: §V · pp. 7–8